IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Data mining

Elysium Technologies Private Limited
ISO 9001:2008 A leading Research and Development Division
Madurai | Chennai | Trichy | Coimbatore | Kollam| Singapore
Website: elysiumtechnologies.com, elysiumtechnologies.info
Email: info@elysiumtechnologies.com

IEEE Final Year Project List 2011-2012

Abstract DATA ENGINEERING 2011 - 2012

01 Dual Framework and Algorithms for Targeted Online Data Delivery

A variety of emerging online data delivery applications challenge existing techniques for data delivery to human users,
applications, or middleware that are accessing data from multiple autonomous servers. In this paper, we develop a
framework for formalizing and comparing pull-based solutions and present dual optimization approaches. The first
approach, most commonly used nowadays, maximizes user utility under the strict setting of meeting a priori constraints
on the usage of system resources. We present an alternative and more flexible approach that maximizes user utility by
satisfying all users. It does this while minimizing the usage of system resources. We discuss the benefits of this latter
approach and develop an adaptive monitoring solution Satisfy User Profiles (SUPs). Through formal analysis, we identify
sufficient optimality conditions for SUP. Using real (RSS feeds) and synthetic traces, we empirically analyze the behavior
of SUP under varying conditions. Our experiments show that we can achieve a high degree of satisfaction of user utility
when the estimations of SUP closely estimate the real event stream, and has the potential to save a significant amount of
system resources. We further show that SUP can exploit feedback to improve user utility with only a moderate increase in
resource utilization.

02 A Flexible Data and Sensor A Fast Multiple Longest Common Subsequence (MLCS) Algorithm

How to achieve a flexible data and sensor planning service to schedule, plan, and empower diverse sensors and
heterogeneous data ordering systems is a big challenge. In this paper, a service-oriented framework of data and sensor
planning service for virtual sensors is proposed. The framework includes an Open Geospatial Consortium (OGC)-compliant
Sensor Planning Service (SPS), a Web Notification Service (WNS), a Sensor Observation Service (SOS), and virtual sensors.
There are two important key technologies in this framework, namely a flexible SPS middleware and an asynchronous
message notification mechanism. The flexible SPS middleware, based on a configuration file and standard interfaces, is
adopted to integrate virtual sensors into a sensor Web. A WNS-based asynchronous notification middleware is used to
inform the user of the status of a task that may need midterm or long-term actions. The framework has been successfully
demonstrated in application scenarios for Simplified General Perturbations Satellite Orbit Model 4 (SGP4) and Earth
Observation System Clearing HOuse (ECHO). The results show that the proposed method has the following improvements
over the existing SPS implementation: a uniform planning service for more satellites, a seamless connection with data
order systems, and a flexible service-oriented framework for virtual sensors.

03 A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification

Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. In this
paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering. The words in the
feature vector of a document set are grouped into clusters, based on similarity test. Words that are similar to each
other are grouped into the same cluster. Each cluster is characterized by a membership function with statistical
mean and deviation. When all the words have been fed in, a desired number of clusters are formed automatically. We
then have one extracted feature for each cluster. The extracted feature, corresponding to a cluster, is a weighted
combination of the words contained in the cluster. By this algorithm, the derived membership functions match
closely with and describe properly the real distribution of the training data. Besides, the user need not specify the
number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted

Madurai Trichy Kollam
Elysium Technologies Private Limited Elysium Technologies Private Limited Elysium Technologies Private Limited
230, Church Road, Annanagar, 3rd Floor,SI Towers, Surya Complex,Vendor junction,
Madurai , Tamilnadu – 625 020. 15 ,Melapudur , Trichy, kollam,Kerala – 691 010.
Contact : 91452 4390702, 4392702, 4394702. Tamilnadu – 620 001. Contact : 91474 2723622.
eMail: info@elysiumtechnologies.com Contact : 91431 - 4002234. eMail: elysium.kollam@gmail.com
eMail: elysium.trichy@gmail.com
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features can then be avoided. Experimental results show that our method can run faster and obtain better extracted
features than other methods.

04 A Generic Multilevel Architecture for Time Series Prediction

Rapidly evolving businesses generate massive amounts of time-stamped data sequences and cause a demand for both
univariate and multivariate time series forecasting. For such data, traditional predictive models based on autoregression
are often not sufficient to capture complex nonlinear relationships between multidimensional features and the time series
outputs. In order to exploit these relationships for improved time series forecasting while also better dealing with a wider
variety of prediction scenarios, a forecasting system requires a flexible and generic architecture to accommodate and tune
various individual predictors as well as combination methods. In reply to this challenge, an architecture for combined,
multilevel time series prediction is proposed, which is suitable for many different universal regressors and combination
methods. The key strength of this architecture is its ability to build a diversified ensemble of individual predictors that form
an input to a multilevel selection and fusion process before the final optimized output is obtained. Excellent generalization
ability is achieved due to the highly boosted complementarity of individual models further enforced through cross-
validation-linked training on exclusive data subsets and ensemble output postprocessing. In a sample configuration with
basic neural network predictors and a mean combiner, the proposed system has been evaluated in different scenarios and
showed a clear prediction performance gain.

05 A Link Analysis Extension of Correspondence Analysis for Mining Relational Databases

This work introduces a link analysis procedure for discovering relationships in a relational database or a graph,
generalizing both simple and multiple correspondence analysis. It is based on a random walk model through the
database defining a Markov chain having as many states as elements in the database. Suppose we are interested in
analyzing the relationships between some elements (or records) contained in two different tables of the relational
database. To this end, in a first step, a reduced, much smaller, Markov chain containing only the elements of interest
and preserving the main characteristics of the initial chain, is extracted by stochastic complementation [41]. This
reduced chain is then analyzed by projecting jointly the elements of interest in the diffusion map subspace [42] and
visualizing the results. This two-step procedure reduces to simple correspondence analysis when only two tables are
defined, and to multiple correspondence analysis when the database takes the form of a simple star-schema. On the
other hand, a kernel version of the diffusion map distance, generalizing the basic diffusion map distance to directed
graphs, is also introduced and the links with spectral clustering are discussed. Several data sets are analyzed by
using the proposed methodology, showing the usefulness of the technique for extracting relationships in relational
databases or graphs.

06 A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in
personalized web information gathering. However, when representing user profiles, many models have utilized only
knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is
proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from
both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against
benchmark models in web information gathering. The results show that this ontology model is successful

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07 Adaptive Cluster Distance Bounding for High-Dimensional Indexing

We consider approaches for similarity search in correlated, high-dimensional data sets, which are derived within a
clustering framework. We note that indexing by “vector approximation” (VA-File), which was proposed as a
technique to combat the “Curse of Dimensionality,” employs scalar quantization, and hence necessarily ignores
dependencies across dimensions, which represents a source of suboptimality. Clustering, on the other hand,
exploits interdimensional correlations and is thus a more compact representation of the data set. However, existing
methods to prune irrelevant clusters are based on bounding hyperspheres and/or bounding rectangles, whose lack
of tightness compromises their efficiency in exact nearest neighbor search. We propose a new cluster-adaptive
distance bound based on separating hyperplane boundaries of Voronoi clusters to complement our cluster based
index. This bound enables efficient spatial filtering, with a relatively small preprocessing storage overhead and is
applicable to euclidean and Mahalanobis similarity measures. Experiments in exact nearest-neighbor set retrieval,
conducted on real data sets, show that our indexing method is scalable with data set size and data dimensionality
and outperforms several recently proposed indexes. Relative to the VA-File, over a wide range of quantization
resolutions, it is able to reduce random IO accesses, given (roughly) the same amount of sequential IO operations,
by factors reaching 100X and more.

08 Anonymous Publication of Sensitive Transactional Data

Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to
enforce privacy-preserving paradigms, such as k-anonymity and ‘-diversity, while minimizing the information loss incurred
in the anonymizing process (i.e., maximize data utility). Existing techniques work well for fixed-schema data, with low
dimensionality. Nevertheless, certain applications require privacy-preserving publishing of transactional data (or basket
data), which involve hundreds or even thousands of dimensions, rendering existing methods unusable. We propose two
categories of novel anonymization methods for sparse high-dimensional data. The first category is based on approximate
nearest-neighbor (NN) search in high-dimensional spaces, which is efficiently performed through locality-sensitive hashing
(LSH). In the second category, we propose two data transformations that capture the correlation in the underlying data: 1)
reduction to a band matrix and 2) Gray encoding-based sorting. These representations facilitate the formation of
anonymized groups with low information loss, through an efficient linear-time heuristic. We show experimentally, using
real-life data sets, that all our methods clearly outperform existing state of the art. Among the proposed techniques, NN-
search yields superior data utility compared to the band matrix transformation, but incurs higher computational overhead.
The data transformation based on Gray code sorting performs best in terms of both data utility and execution time.

09 Answering Frequent Probabilistic Inference Queries in Databases

Existing solutions for probabilistic inference queries mainly focus on answering a single inference query, but seldom
address the issues of efficiently returning results for a sequence of frequent queries, which is more popular and
practical in many real applications. In this paper, we mainly study the computation caching and sharing among a
sequence of inference queries in databases. The clique tree propagation (CTP) algorithm is first introduced in
databases for probabilistic inference queries. We use the materialized views to cache the intermediate results of the
previous inference queries, which might be shared with the following queries, and consequently reduce the time
cost. Moreover, we take the query workload into account to identify the frequently queried variables. To optimize
probabilistic inference queries with CTP, we cache these frequent query variables into the materialized views to
maximize the reuse. Due to the existence of different query plans, we present heuristics to estimate costs and select
the optimal query plan. Finally, we present the experimental evaluation in relational databases to illustrate the validity
and superiority of our approaches in answering frequent probabilistic inference queries.

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10 Authenticated Multistep Nearest Neighbor Search

Multistep processing is commonly used for nearest neighbor (NN) and similarity search in applications involving
highdimensional data and/or costly distance computations. Today, many such applications require a proof of result
correctness. In this setting, clients issue NN queries to a server that maintains a database signed by a trusted authority.
The server returns the NN set along with supplementary information that permits result verification using the data set
signature. An adaptation of the multistep NN algorithm incurs prohibitive network overhead due to the transmission of false
hits, i.e., records that are not in the NN set, but are nevertheless necessary for its verification. In order to alleviate this
problem, we present a novel technique that reduces the size of each false hit. Moreover, we generalize our solution for a
distributed setting, where the database is horizontally partitioned over several servers. Finally, we demonstrate the
effectiveness of the proposed solutions with real data sets of various dimensionalities.

11 Automatic Discovery of Personal Name Aliases from the Web

An individual is typically referred by numerous name aliases on the web. Accurate identification of aliases of a given
person name is useful in various web related tasks such as information retrieval, sentiment analysis, personal name
disambiguation, and relation extraction. We propose a method to extract aliases of a given personal name from the
web. Given a personal name, the proposed method first extracts a set of candidate aliases. Second, we rank the
extracted candidates according to the likelihood of a candidate being a correct alias of the given name. We propose a
novel, automatically extracted lexical pattern-based approach to efficiently extract a large set of candidate aliases
from snippets retrieved from a web search engine. We define numerous ranking scores to evaluate candidate aliases
using three approaches: lexical pattern frequency, word co-occurrences in an anchor text graph, and page counts on
the web. To construct a robust alias detection system, we integrate the different ranking scores into a single ranking
function using ranking support vector machines. We evaluate the proposed method on three data sets: an English
personal names data set, an English place names data set, and a Japanese personal names data set. The proposed
method outperforms numerous baselines and previously proposed name alias extraction methods, achieving a
statistically significant mean reciprocal rank (MRR) of 0.67. Experiments carried out using location names and
Japanese personal names suggest the possibility of extending the proposed method to extract aliases for different
types of named entities, and for different languages. Moreover, the aliases extracted using the proposed method are
successfully utilized in an information retrieval task and improve recall by 20 percent in a relationdetection task.

12 Geospatial Automatic Enrichment of Semantic Relation Network and Its Application to Word Sense Disambiguation

The most fundamental step in semantic information processing (SIP) is to construct knowledge base (KB) at the human
level; that is to the general understanding and conception of human knowledge. WordNet has been built to be the most
systematic and as close to the human level and is being applied actively in various works. In one of our previous research,
we found that a semantic gap exists between concept pairs of WordNet and those of real world. This paper contains a study
on the enrichment method to build a KB. We describe the methods and the results for the automatic enrichment of the
semantic relation network. A rule based method using WordNet’s glossaries and an inference method using axioms for
WordNet relations are applied for the enrichment and an enriched WordNet (E-WordNet) is built as the result. Our
experimental results substantiate the usefulness of E-WordNet. An evaluation by comparison with the human level is
attempted. Moreover, WSD-SemNet, a new word sense disambiguation (WSD) method in which E-WordNet is applied, is
proposed and evaluated by comparing it with the state-of-the-art algorithm..

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13 Branch-and-Bound for Model Selection and Its Computational Complexity

Branch-and-bound methods are used in various data analysis problems, such as clustering, seriation and feature
selection. Classical approaches of branch-and-bound based clustering search through combinations of various
partitioning possibilities to optimize a clustering cost. However, these approaches are not practically useful for
clustering of image data where the size of data is large. Additionally, the number of clusters is unknown in most of
the image data analysis problems. By taking advantage of the spatial coherency of clusters, we formulate an
innovative branch-and-bound approach, which solves clustering problem as a model-selection problem. In this
generalized approach, cluster parameter candidates are first generated by spatially coherent sampling. A branch-
andbound search is carried out through the candidates to select an optimal subset. This paper formulates this
approach and investigates its average computational complexity. Improved clustering quality and robustness to
outliers compared to conventional iterative approach are demonstrated with experiments.

14 Measuring Client-Perceived Pageview Response Time of Internet Services

As e-commerce services are exponentially growing, businesses need quantitative estimates of client-perceived response
times to continuously improve the quality of their services. Current server-side nonintrusive measurement techniques are
limited to nonsecured HTTP traffic. In this paper, we present the design and evaluation a monitor, namely sMonitor, which
is able to measure client-perceived response times for both HTTP and HTTPS traffic. At the heart of sMonitor is a novel
size-based analysis method that parses live packets to delimit different webpages and to infer their response times. The
method is based on the observation that most HTTP(S)-compatible browsers send significantly larger requests for
container objects than those for embedded objects. sMonitor is designed to operate accurately in the presence of
complicated browser behaviors, such as parallel downloading of multiple webpages and HTTP pipelining, as well as packet
losses and delays. It requires only to passively collect network traffic in and out of the monitored secured services. We
conduct comprehensive experiments across a wide range of operating conditions using live secured Internet services, on
the PlanetLab, and on controlled networks. The experimental results demonstrate that sMonitor is able to control the
estimation error within 6.7 percent, in comparison with the actual measured time at the client side.

15 Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints

Most existing data stream classification techniques ignore one important aspect of stream data: arrival of a novel
class. We address this issue and propose a data stream classification technique that integrates a novel class
detection mechanism into traditional classifiers, enabling automatic detection of novel classes before the true labels
of the novel class instances arrive. Novel class detection problem becomes more challenging in the presence of
concept-drift, when the underlying data distributions evolve in streams. In order to determine whether an instance
belongs to a novel class, the classification model sometimes needs to wait for more test instances to discover
similarities among those instances. A maximum allowable wait time Tc is imposed as a time constraint to classify a
test instance. Furthermore, most existing stream classification approaches assume that the true label of a data point
can be accessed immediately after the data point is classified. In reality, a time delay Tl is involved in obtaining the
true label of a data point since manual labeling is time consuming. We show how to make fast and correct
classification decisions under these constraints and apply them to real benchmark data. Comparison with state-of-
the-art stream classification techniques proves the superiority of our approach.

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16 Classification Using Streaming Random Forests

We consider the problem of data stream classification, where the data arrive in a conceptually infinite stream, and the
opportunity to examine each record is brief. We introduce a stream classification algorithm that is online, running in
amortized Oð1Þ time, able to handle intermittent arrival of labeled records, and able to adjust its parameters to respond to
changing class boundaries (“concept drift”) in the data stream. In addition, when blocks of labeled data are short, the
algorithm is able to judge internally whether the quality of models updated from them is good enough for deployment on
unlabeled records, or whether further labeled records are required. Unlike most proposed stream-classification algorithms,
multiple target classes can be handled. Experimental results on real and synthetic data show that accuracy is comparable
to a conventional classification algorithm that sees all of the data at once and is able to make multiple passes over it.

17 CoFiDS: A Belief-Theoretic Approach for Automated Collaborative Filtering

Automated Collaborative Filtering (ACF) refers to a group of algorithms used in recommender systems, a research topic
that has received considerable attention due to its e-commerce applications. However, existing techniques are rarely
capable of dealing with imperfections in user-supplied ratings. When such imperfections (e.g., ambiguities) cannot be
avoided, designers resort to simplifying assumptions that impair the system’s performance and utility. We have developed
a novel technique referred to as CoFiDS—Collaborative Filtering based on Dempster-Shafer belief-theoretic framework—
that can represent a wide variety of data imperfections, propagate them throughout the decision-making process without
the need to make simplifying assumptions, and exploit contextual information. With its DS-theoretic predictions, the
domain expert can either obtain a “hard” decision or can narrow the set of possible predictions to a smaller set. With its
capability to handle data imperfections, CoFiDS widens the applicability of ACF to such critical and sensitive domains as
medical decision support systems and defense-related applications. We describe the theoretical foundation of the system
and report experiments with a benchmark movie data set. We explore some essential aspects of CoFiDS’ behavior and
show that its performance compares favorably with other ACF systems

18 Collaborative Filtering with Personalized Skylines

Collaborative filtering (CF) systems exploit previous ratings and similarity in user behavior to recommend the top-k objects/
records which are potentially most interesting to the user assuming a single score per object. However, in various
applications, a record (e.g., hotel) maybe rated on several attributes (value, service, etc.), in which case simply returning the
ones with the highest overall scores fails to capture the individual attribute characteristics and to accommodate different
selection criteria. In order to enhance the flexibility of CF, we propose Collaborative Filtering Skyline (CFS), a general
framework that combines the advantages of CF with those of the skyline operator. CFS generates a personalized skyline for
each user based on scores of other users with similar behavior. The personalized skyline includes objects that are good on
certain aspects, and eliminates the ones that are not interesting on any attribute combination. Although the integration of
skylines and CF has several attractive properties, it also involves rather expensive computations. We face this challenge
through a comprehensive set of algorithms and optimizations that reduce the cost of generating personalized skylines. In
addition to exact skyline processing, we develop an approximate method that provides error guarantees. Finally, we
propose the top-k personalized skyline, where the user specifies the required output cardinality

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19 Comprehensive Citation Index for Research Networks

The existing Science Citation Index only counts direct citations, whereas PageRank disregards the number of direct
citations. We propose a new Comprehensive Citation Index (CCI) that evaluates both direct and indirect intellectual
influence of research papers, and show that CCI is more reliable in discovering research papers with far-reaching
influence.

20 Constrained Skyline Query Processing against Distributed Data.

The skyline of a multidimensional point set is a subset of interesting points that are not dominated by others. In this paper,
we investigate constrained skyline queries in a large-scale unstructured distributed environment, where relevant data are
distributed among geographically scattered sites. We first propose a partition algorithm that divides all data sites into
incomparable groups such that the skyline computations in all groups can be parallelized without changing the final result.
We then develop a novel algorithm framework called PaDSkyline for parallel skyline query processing among partitioned
site groups. We also employ intragroup optimization and multifiltering technique to improve the skyline query processes
within each group. In particular, multiple (local) skyline points are sent together with the query as filtering points, which
help identify unqualified local skyline points early on a data site. In this way, the amount of data to be transmitted via
network connections is reduced, and thus, the overall query response time is shortened further. Cost models and heuristics
are proposed to guide the selection of a given number of filtering points from a superset. A costefficient model is
developed to determine how many filtering points to use for a particular data site.

21 Continuous Monitoring of Distance-Based Range Queries

Given a positive value r, a distance-based range query returns the objects that lie within the distance r of the query
location. In this paper, we focus on the distance-based range queries that continuously change their locations in a
euclidean space. We present an efficient and effective monitoring technique based on the concept of a safe zone.
The safe zone of a query is the area with a property that while the query remains inside it, the results of the query
remain unchanged. Hence, the query does not need to be reevaluated unless it leaves the safe zone. Our
contributions are as follows: 1) We propose a technique based on powerful pruning rules and a unique access order
which efficiently computes the safe zone and minimizes the I/O cost. 2) We theoretically determine and
experimentally verify the expected distance a query moves before leaving the safe zone and, for majority of queries,
the expected number of guard objects. 3) Our experiments demonstrate that the proposed approach is close to
optimal and is an order of magnitude faster than a naı¨ve algorithm. 4) We also extend our technique to monitor the
queries in a road network. Our algorithm is up to two order of magnitude faster than a naı¨ve algorithm.

22 Cosdes: A Collaborative Spam Detection System with a Novel E-Mail Abstraction Scheme

The E-mail communication is indispensable nowadays, but the e-mail spam problem continues growing drastically. In
recent years, the notion of collaborative spam filtering with near-duplicate similarity matching scheme has been widely
discussed. The primary idea of the similarity matching scheme for spam detection is to maintain a known spam database,
formed by user feedback, to block subsequent near-duplicate spams. On purpose of achieving efficient similarity matching
and reducing storage utilization, prior works mainly represent each e-mail by a succinct abstraction derived from e-mail
content text. However, these abstractions of e-mails cannot fully catch the evolving nature of spams, and are thus not
effective enough in near-duplicate detection. In this paper, we propose a novel e-mail abstraction scheme, which considers

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e-mail layout structure to represent e-mails. We present a procedure to generate the e-mail abstraction using HTML content
in e-mail, and this newly devised abstraction can more effectively capture the near-duplicate phenomenon of spams.
Moreover, we design a complete spam detection system Cosdes (standing for COllaborative Spam DEtection System),
which possesses an efficient near-duplicate matching scheme and a progressive update scheme. The progressive update
scheme enables system Cosdes to keep the most up-to-date information for near-duplicate detection. We evaluate Cosdes
on a live data set collected from a real e-mail server and show that our system outperforms the prior approaches in
detection results and is applicable to the real world.

23 Coupling Logical Analysis of Data and Shadow Clustering for Partially Defined Positive Boolean Function Reconstruction

The problem of reconstructing the AND-OR expression of a partially defined positive Boolean function (pdpBf) is
solved by adopting a novel algorithm, denoted by LSC, which combines the advantages of two efficient techniques,
Logical Analysis of Data (LAD) and Shadow Clustering (SC). The kernel of the approach followed by LAD consists in
a breadth-first enumeration of all the prime implicants whose degree is not greater than a fixed maximum d. In
contrast, SC adopts an effective heuristic procedure for retrieving the most promising logical products to be
included in the resulting AND-OR expression. Since the computational cost required by LAD prevents its application
even for relatively small dimensions of the input domain, LSC employs a depth-first approach, with asymptotically
linear memory occupation, to analyze the prime implicants having degree not greater than d. In addition, the
theoretical analysis proves that LSC presents almost the same asymptotic time complexity as LAD. Extensive
simulations on artificial benchmarks validate the good behavior of the computational cost exhibited by LSC, in
agreement with the theoretical analysis. Furthermore, the pdpBf retrieved by LSC always shows a better
performance, in terms of complexity and accuracy, with respect to those obtained by LAD.

24 Data Leakage Detection

We study the following problem: A data distributor has given sensitive data to a set of supposedly trusted agents (third
parties). Some of the data are leaked and found in an unauthorized place (e.g., on the web or somebody’s laptop). The
distributor must assess the likelihood that the leaked data came from one or more agents, as opposed to having been
independently gathered by other means. We propose data allocation strategies (across the agents) that improve the
probability of identifying leakages. These methods do not rely on alterations of the released data (e.g., watermarks). In
some cases, we can also inject “realistic but fake” data records to further improve our chances of detecting leakage and
identifying the guilty party.

25 Decision Trees for Uncertain Data

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers
to handle data with uncertain information. Value uncertainty arises in many applications during the data collection
process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple
repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but
by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives
(such as mean and median), we discover that the accuracy of a decision tree classifier can be much improved if the
“complete information” of a data item (taking into account the probability density function (pdf)) is utilized. We
extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments
have been conducted which show that the resulting classifiers are more accurate than those using value averages.
Since processing pdfs is computationally more costly than processing single values (e.g., averages), decision tree
construction on uncertain data is more CPU demanding than that for certain data. To tackle this problem, we propose
a series of pruning techniques that can greatly improve construction efficiency.

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26 Design and Implementation of an Intrusion Response System for Relational Databases

The intrusion response component of an overall intrusion detection system is responsible for issuing a suitable response
to an anomalous request. We propose the notion of database response policies to support our intrusion response system
tailored for a DBMS. Our interactive response policy language makes it very easy for the database administrators to specify
appropriate response actions for different circumstances depending upon the nature of the anomalous request. The two
main issues that we address in context of such response policies are that of policy matching, and policy administration. For
the policy matching problem, we propose two algorithms that efficiently search the policy database for policies that match
an anomalous request. We also extend the PostgreSQL DBMS with our policy matching mechanism, and report
experimental results. The experimental evaluation shows that our techniques are very efficient. The other issue that we
address is that of administration of response policies to prevent malicious modifications to policy objects from legitimate
users. We propose a novel Joint Threshold Administration Model (JTAM) that is based on the principle of separation of
duty. The key idea in JTAM is that a policy object is jointly administered by at least k database administrator (DBAs), that is,
any modification made to a policy object will be invalid unless it has been authorized by at least k DBAs. We present design
details of JTAM which is based on a cryptographic threshold signature scheme, and show how JTAM prevents malicious
modifications to policy objects from authorized users. We also implement JTAM in the PostgreSQL DBMS, and report
experimental results on the efficiency of our techniques.

27 Differential Privacy via Wavelet Transforms

Privacy-preserving data publishing has attracted considerable research interest in recent years. Among the existing
solutions, ˇ-differential privacy provides the strongest privacy guarantee. Existing data publishing methods that
achieve ˇ-differential privacy, however, offer little data utility. In particular, if the output data set is used to answer
count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders
the results useless. In this paper, we develop a data publishing technique that ensures ˇ-differential privacy while
providing accurate answers for range-count queries, i.e., count queries where the predicate on each attribute is a
range. The core of our solution is a framework that applies wavelet transforms on the data before adding noise to it.
We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical
analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data,
we show the effectiveness and efficiency of our solution.

28 Discovering Activities to Recognize and Track in a Smart Environment

The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for
providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to
monitor the functional health of smart home residents, we need to design technologies that recognize and track activities
that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the
approaches are applied to activities that have been preselected and for which labeled training data are available. In
contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in
an individual’s routine. With this capability, we can then track the occurrence of regular activities to monitor functional
health and to detect changes in an individual’s patterns and lifestyle. In this paper, we describe our activity mining and
tracking approach, and validate our algorithms on data collected in physical smart environments.

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29 Discovering Conditional Functional Dependencies

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension
of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules
for cleaning relational data. However, finding quality CFDs is an expensive process that involves intensive manual
effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from relations. Already
hard for traditional FDs, the discovery problem is more difficult for CFDs. Indeed, mining patterns in CFDs introduces
new challenges. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on
techniques for mining closed item sets, and is used to discover constant CFDs, namely, CFDs with constant patterns
only. Constant CFDs are particularly important for object identification, which is essential to data cleaning and data
integration. The other two algorithms are developed for discovering general CFDs. One algorithm, referred to as
CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as
FastCFD, is based on the depth-first approach used in FastFD, a method for discovering FDs. It leverages closed-
item-set mining to reduce the search space. As verified by our experimental study, CFDMiner can be multiple orders
of magnitude faster than CTANE and FastCFD for constant CFD discovery. CTANE works well when a given relation
is large, but it does not scale well with the arity of the relation. FastCFD is far more efficient than CTANE when the
arity of the relation is large; better still, leveraging optimization based on closed-item-set mining, FastCFD also
scales well with the size of the relation. These algorithms provide a set of cleaning-rule discovery tools for users to
choose for different applications.

30 Effective Navigation of Query Results Based on Concept Hierarchies

Search queries on biomedical databases, such as PubMed, often return a large number of results, only a small
subset of which is relevant to the user. Ranking and categorization, which can also be combined, have been
proposed to alleviate this information overload problem. Results categorization for biomedical databases is the focus
of this work. A natural way to organize biomedical citations is according to their MeSH annotations. MeSH is a
comprehensive concept hierarchy used by PubMed. In this paper, we present the BioNav system, a novel search
interface that enables the user to navigate large number of query results by organizing them using the MeSH concept
hierarchy. First, the query results are organized into a navigation tree. At each node expansion step, BioNav reveals
only a small subset of the concept nodes, selected such that the expected user navigation cost is minimized. In
contrast, previous works expand the hierarchy in a predefined static manner, without navigation cost modeling. We
show that the problem of selecting the best concepts to reveal at each node expansion is NP-complete and propose
an efficient heuristic as well as a feasible optimal algorithm for relatively small trees. We show experimentally that
BioNav outperforms state-of-the-art categorization systems by up to an order of magnitude, with respect to the user
navigation cost.

31 Efficient Periodicity Mining in Time Series Databases Using Suffix Trees

Periodic pattern mining or periodicity detection has a number of applications, such as prediction, forecasting,
detection of unusual activities, etc. The problem is not trivial because the data to be analyzed are mostly noisy and
different periodicity types (namely symbol, sequence, and segment) are to be investigated. Accordingly, we argue
that there is a need for a comprehensive approach capable of analyzing the whole time series or in a subsection of it
to effectively handle different types of noise (to a certain degree) and at the same time is able to detect different
types of periodic patterns; combining these under one umbrella is by itself a challenge. In this paper, we present an
algorithm which can detect symbol, sequence (partial), and segment (full cycle) periodicity in time series. The
algorithm uses suffix tree as the underlying data structure; this allows us to design the algorithm such that its

10


worstcase complexity is Oðk:n2Þ, where k is the maximum length of periodic pattern and n is the length of the
analyzed portion (whole or subsection) of the time series. The algorithm is noise resilient; it has been successfully
demonstrated to work with replacement, insertion, deletion, or a mixture of these types of noise. We have tested the
proposed algorithm on both synthetic and real data from different domains, including protein sequences. The
conducted comparative study demonstrate the applicability and effectiveness of the proposed algorithm; it is
generally more time-efficient and noise-resilient than existing algorithms.

32 A Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns

Nowadays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable,
relevance feedback techniques were incorporated into CBIR such that more precise results can be obtained by taking
user’s feedbacks into account. However, existing relevance feedback-based CBIR methods usually request a number of
iterative feedbacks to produce refined search results, especially in a large-scale image database. This is impractical and
inefficient in real applications. In this paper, we propose a novel method, Navigation-Pattern-based Relevance Feedback
(NPRF), to achieve the high efficiency and effectiveness of CBIR in coping with the large-scale image data. In terms of
efficiency, the iterations of feedback are reduced substantially by using the navigation patterns discovered from the user
query log. In terms of effectiveness, our proposed search algorithm NPRFSearch makes use of the discovered navigation
patterns and three kinds of query refinement strategies, Query Point Movement (QPM), Query Reweighting (QR), and Query
Expansion (QEX), to converge the search space toward the user’s intention effectively. By using NPRF method, high quality
of image retrieval on RF can be achieved in a small number of feedbacks. The experimental results reveal that NPRF
outperforms other existing methods significantly in terms of precision, coverage, and number of feedbacks.

33 Efficient Techniques for Online Record Linkage

The need to consolidate the information contained in heterogeneous data sources has been widely documented in
recent years. In order to accomplish this goal, an organization must resolve several types of heterogeneity problems,
especially the entity heterogeneity problem that arises when the same real-world entity type is represented using
different identifiers in different data sources. Statistical record linkage techniques could be used for resolving this
problem. However, the use of such techniques for online record linkage could pose a tremendous communication
bottleneck in a distributed environment (where entity heterogeneity problems are often encountered). In order to
resolve this issue, we develop a matching tree, similar to a decision tree, and use it to propose techniques that
reduce the communication overhead significantly, while providing matching decisions that are guaranteed to be the
same as those obtained using the conventional linkage technique. These techniques have been implemented, and
experiments with real-world and synthetic databases show significant reduction in communication overhead.

34 Efficient Top-k Approximate Subtree Matching in Small Memory

We consider the Top-k Approximate Subtree Matching (TASM) problem: finding the k best matches of a small query tree
within a large document tree using the canonical tree edit distance as a similarity measure between subtrees. Evaluating
the tree edit distance for large XML trees is difficult: the best known algorithms have cubic runtime and quadratic space
complexity, and, thus, do not scale. Our solution is TASM-postorder, a memory-efficient and scalable TASM algorithm. We
prove an upper bound for the maximum subtree size for which the tree edit distance needs to be evaluated. The upper
bound depends on the query and is independent of the document size and structure. A core problem is to efficiently prune
subtrees that are above this size threshold. We develop an algorithm based on the prefix ring buffer that allows us to prune
all subtrees above the threshold in a single postorder scan of the document. The size of the prefix ring buffer is linear in the
threshold. As a result, the space complexity of TASM-postorder depends only on k and the query size, and the runtime of
TASM-postorder is linear in the size of the document. Our experimental evaluation on large synthetic and real XML

11


documents confirms our analytic results.

35 Energy Time Series Forecasting Based on Pattern Sequence Similarity

This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences.
First, clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the
prediction of a data point is provided as follows: first, the pattern sequence prior to the day to be predicted is
extracted. Then, this sequence is searched in the historical data and the prediction is calculated by averaging all the
samples immediately after the matched sequence. The main novelty is that only the labels associated with each
pattern are considered to forecast the future behavior of the time series, avoiding the use of real values of the time
series until the last step of the prediction process. Results from several energy time series are reported and the
performance of the proposed method is compared to that of recently published techniques showing a remarkable
improvement in the prediction.

36 Energy Time Series Forecasting Based on Pattern Sequence Similarity

This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. First,
clustering techniques are used with the aim of grouping and labeling the samples from a data set. Thus, the prediction of a
data point is provided as follows: first, the pattern sequence prior to the day to be predicted is extracted. Then, this
sequence is searched in the historical data and the prediction is calculated by averaging all the samples immediately after
the matched sequence. The main novelty is that only the labels associated with each pattern are considered to forecast the
future behavior of the time series, avoiding the use of real values of the time series until the last step of the prediction
process. Results from several energy time series are reported and the performance of the proposed method is compared to
that of recently published techniques showing a remarkable improvement in the prediction.

37 Estimating and Enhancing Real-Time Data Service Delays: Control-Theoretic Approaches

It is essential to process real-time data service requests such as stock quotes and trade transactions in a timely
manner using fresh data, which represent the current real-world phenomena such as the stock market status. Users
may simply leave when the database service delay is excessive. Also, temporally inconsistent data may give an
outdated view of the real-world status. However, supporting the desired timeliness and freshness is challenging due
to dynamic workloads. To address the problem, we present new approaches for 1) database backlog estimation, 2)
fine-grained closed-loop admission control based on the backlog model, and 3) incoming load smoothing. Our
backlog estimation and control-theoretic approaches aim to support the desired service delay bound without
degrading the data freshness, critical for real-time data services. Specifically, we design, implement, and evaluate
two feedback controllers based on linear control theory and fuzzy logic control theory, to meet the desired service
delay. Workload smoothing, under overload, helps the database admit and process more transactions in a timely
fashion by probabilistically reducing the burstiness of incoming data service requests. In terms of the data service
delay and throughput, our closed-loop admission control and probabilistic load smoothing schemes considerably
outperform several baselines in the experiments undertaken in a stock trading database testbed.

38 Experience Transfer for the Configuration Tuning in Large-Scale Computing Systems

This paper proposes a new strategy, the experience transfer, to facilitate the management of large-scale computing
systems. It deals with the utilization of management experiences in one system (or previous systems) to benefit the same

12


management task in other systems (or current systems). We use the system configuration tuning as a case application to
demonstrate all procedures involved in the experience transfer including the experience representation, experience
extraction, and experience embedding. The dependencies between system configuration parameters are treated as
transferable experiences in the configuration tuning for two reasons: 1) because such knowledge is helpful to the efficiency
of the optimal configuration search, and 2) because the parameter dependencies are typically unchanged between two
similar systems. We use the Bayesian network to model configuration dependencies and present a configuration tuning
algorithm based on the Bayesian network construction and sampling. As a result, after the configuration tuning is
completed in the original system, we can obtain a Bayesian network as the by-product which records the dependencies
between system configuration parameters. Such a network is then embedded into the tuning process in other similar
systems as transferred experiences to improve the configuration search efficiency. Experimental results in a web-based
system show that with the help of transferred experiences, the configuration tuning process can be significantly
accelerated.

39 Exploring Application-Level Semantics for Data Compression

This Natural phenomena show that many creatures form large social groups and move in regular patterns. However,
previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first
propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their
movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D,
which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression
algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a
Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction
phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal
solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental
results show that the proposed compression algorithm leverages the group movement patterns to reduce the
amount of delivered data effectively and efficiently.


Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous
works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an
efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in
wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group
movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an
entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the
location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR)
problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules
and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm
leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

41 Finding Correlated Biclusters from Gene Expression Data

Extracting biologically relevant information from DNA microarrays is a very important task for drug development and
test, function annotation, and cancer diagnosis. Various clustering methods have been proposed for the analysis of
gene expression data, but when analyzing the large and heterogeneous collections of gene expression data,
conventional clustering algorithms often cannot produce a satisfactory solution. Biclustering algorithm has been
presented as an alternative approach to standard clustering techniques to identify local structures from gene

13


expression data set. These patterns may provide clues about the main biological processes associated with different
physiological states. In this paper, different from existing bicluster patterns, we first introduce a more general
pattern: correlated bicluster, which has intuitive biological interpretation. Then, we propose a novel transform
technique based on singular value decomposition so that identifying correlated-bicluster problem from gene
expression matrix is transformed into two global clustering problems. The Mixed-Clustering algorithm and the Lift
algorithm are devised to efficiently produce ˇ-corBiclusters. The biclusters obtained using our method from gene
expression data sets of multiple human organs and the yeast Saccharomyces cerevisiae demonstrate clear
biological meanings.

42 Frequent Item Computation on a Chip

This Computing frequent items is an important problem by itself and as a subroutine in several data mining algorithms. In
this paper, we explore how to accelerate the computation of frequent items using field-programmable gate arrays (FPGAs)
with a threefold goal: increase performance over existing solutions, reduce energy consumption over CPU-based systems,
and explore the design space in detail as the constraints on FPGAs are very different from those of traditional software-
based systems. We discuss three design alternatives, each one of them exploiting different FPGA features and each one
providing different performance/scalability trade-offs. An important result of the paper is to demonstrate how the inherent
massive parallelism of FPGAs can improve performance of existing algorithms but only after a fundamental redesign of the
algorithms. Our experimental results show that, e.g., the pipelined solution we introduce can reach more than 100 million
tuples per second of sustained throughput (four times the best available results to date) by making use of techniques that
are not available to CPU-based solutions. Moreover, and unlike in software approaches, the high throughput is independent
of the skew of the Zipf distribution of the input and at a far lower energy cost. paper presents a new approach to forecast
the behavior of time series based on similarity of pattern sequences. First, clustering techniques are used with the aim of
grouping and labeling the samples from a data set. Thus, the prediction of a data point is provided as follows: first, the
pattern sequence prior to the day to be predicted is extracted. Then, this sequence is searched in the historical data and the
prediction is calculated by averaging all the samples immediately after the matched sequence. The main novelty is that only
the labels associated with each pattern are considered to forecast the future behavior of the time series, avoiding the use of
real values of the time series until the last step of the prediction process. Results from several energy time series are
reported and the performance of the proposed method is compared to that of recently published techniques showing a
remarkable improvement in the prediction.

43 Inconsistency-Tolerant Integrity Checking

All methods for efficient integrity checking require all integrity constraints to be totally satisfied, before any update is
executed. However, a certain amount of inconsistency is the rule, rather than the exception in databases. In this
paper, we close the gap between theory and practice of integrity checking, i.e., between the unrealistic theoretical
requirement of total integrity and the practical need for inconsistency tolerance, which we define for integrity
checking methods. We show that most of them can still be used to check whether updates preserve integrity, even if
the current state is inconsistent. Inconsistency-tolerant integrity checking proves beneficial both for integrity
preservation and query answering. Also, we show that it is useful for view updating, repairs, schema evolution, and
other applications.

44 Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or
approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which

14


algorithms are effective for initialization? When should the search process be restarted? In the present work, we investigate
these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian
networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While
the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a
novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture
models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search
algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE
search time for application BNs by several orders of magnitude compared to using uniform at random initialization without
restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree
clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

45 Integration of the HL7 Standard in a Multiagent System to Support Personalized Access to e-Health Services

This Natural phenomena show that many creatures form large social groups and move in regular patterns. However,
previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first
propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their
movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D,
which exploits the obtained group movement patterns to reduce the amount of delivered data. The compression
algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a
Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction
phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal
solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental
results show that the proposed compression algorithm leverages the group movement patterns to reduce the
amount of delivered data effectively and efficiently.


Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous
works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an
efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in
wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group
movement patterns to reduce the amount of delivered data. The compression algorithm includes a sequence merge and an
entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the
location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR)
problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules
and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm
leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.

47 Intertemporal Discount Factors as a Measure of Trustworthiness in Electronic Commerce

In multiagent interactions, such as e-commerce and file sharing, being able to accurately assess the trustworthiness
of others is important for agents to protect themselves from losing utility. Focusing on rational agents in e-
commerce, we prove that an agent’s discount factor (time preference of utility) is a direct measure of the agent’s
trustworthiness for a set of reasonably general assumptions and definitions. We propose a general list of desiderata
for trust systems and discuss how discount factors as trustworthiness meet these desiderata. We discuss how
discount factors are a robust measure when entering commitments that exhibit moral hazards. Using an online
market as a motivating example, we derive some analytical methods both for measuring discount factors and for
aggregating the measurements.

15



48 IR-Tree: An Efficient Index for Geographic Document Search

Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves
documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks
the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index
that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search
engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that
together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial
filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-
tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus
caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been
conducted and the experiment results demonstrate that IR-tree outperforms the state-of-theart approaches for geographic
document searches.

49 Knowledge Discovery in Services (KDS): Aggregating Software Services to Discover Enterprise Mashups

Service mashup is the act of integrating the resulting data of two complementary software services into a common
picture. Such an approach is promising with respect to the discovery of new types of knowledge. However, before
service mashup routines can be executed, it is necessary to predict which services (of an open repository) are viable
candidates. Similar to Knowledge Discovery in Databases (KDD), we introduce the Knowledge Discovery in Services
(KDS) process that identifies mashup candidates. In this work, the KDS process is specialized to address a
repository of open services that do not contain semantic annotations. In these situations, specialized techniques are
required to determine equivalences among open services with reasonable precision. This paper introduces a bottom-
up process for KDS that adapts to the environment of services for which it operates. Detailed experiments are
discussed that evaluate KDS techniques on an open repository of services from the Internet and on a repository of
services created in a controlled environment.

50 Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction

Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis
(LDA) is a traditional algorithm for supervised feature extraction. Recently, unlabeled data have been utilized to improve
LDA. However, the intrinsic problems of LDA still exist and only the similarity among the unlabeled data is utilized. In this
paper, we propose a novel algorithm, called Semisupervised Semi-Riemannian Metric Map (S3RMM), following the
geometric framework of semi- Riemannian manifolds. S3RMM maximizes the discrepancy of the separability and similarity
measures of scatters formulated by using semi-Riemannian metric tensors. The metric tensor of each sample is learned via
semisupervised regression. Our method can also be a general framework for proposing new semisupervised algorithms,
utilizing the existing discrepancy-criterion-based algorithms. The experiments demonstrated on faces and handwritten
digits show that S3RMM is promising for semisupervised feature extraction.

51 Load Shedding in Mobile Systems with MobiQual

In location-based, mobile continual query (CQ) systems, two key measures of quality-of-service (QoS) are: freshness
and accuracy. To achieve freshness, the CQ server must perform frequent query reevaluations. To attain accuracy,

16

IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Data mining

IEEE Final Year Projects 2011-2012 :: Elysium Technologies Pvt Ltd::Data mining

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