BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docx

BIG IOT AND SOCIAL NETWORKING DATA FOR SMART
CITIES:
Algorithmic improvements on Big Data Analysis in the context
of RADICAL city
applications
Evangelos Psomakelis12,Fotis Aisopos1, Antonios Litke1,
Konstantinos Tserpes21, Magdalini
Kardara1 and Pablo Martínez Campo3
1Distributed Knowledge and Media Systems Group, National
Technical University of Athens, Zografou Campus, Athens,
Greece
2Informatics and Telematics Dept, Harokopio University of
Athens, Greece
3Communications Engineering department, University of
Cantabria, Santander, Spain
{fotais, litke, nkardara, tserpes,
vpsomak}@mail.ntua.gr,[email protected]
Keywords: Internet of Things, Social Networking, Big Data
Aggregation and Analysis, Smart City applications,
Sentiment Analysis, Machine Learning
Abstract: In this paper we present a SOA (Service Oriented
Architecture)-based platform, enabling the retrieval and

analysis of big datasets stemming from social networking (SN)
sites and Internet of Things (IoT) devices,
collected by smart city applications and socially-aware data
aggregation services. A large set of city
applications in the areas of Participating Urbanism, Augmented
Reality and Sound-Mapping throughout
participating cities is being applied, resulting into produced sets
of millions of user-generated events and
online SN reports fed into the RADICAL platform. Moreover,
we study the application of data analytics such
as sentiment analysis to the combined IoT and SN data saved
into an SQL database, further investigating
algorithmic and configurations to minimize delays in dataset
processing and results retrieval.
1 INTRODUCTION
Modern cities are increasingly turning towards
ICT technology for confronting pressures associated
with demographic changes, urbanization, climate
change (Romero Lankao, 2008) and globalization.
Therefore, most cities have undertaken significant
investments during the last decade in ICT
infrastructure including computers, broadband

connectivity and recently sensing infrastructures.
These infrastructures have empowered a number of
innovative services in areas such as participatory
sensing, urban logistics and ambient assisted living.
Such services have been extensively deployed in
several cities, thereby demonstrating the potential
benefits of ICT infrastructures for businesses and the
citizens themselves. During the last few years we
have also witnessed an explosion of sensor
deployments and social networking services, along
with the emergence of social networking (Conti et al.,
2011) and internet‐of‐things technologies (Perera et
al., 2013; Sundmaeker et al., 2010) Social and sensor
networks can be combined in order to offer a variety
of added‐value services for smart cities, as has
already been demonstrated by various early internet‐
of‐things applications (such as WikiCity(Calabrese et
al., 2007), CitySense(Murty et al., 2007),
GoogleLatitude(Page and Kobsa, 2010)), as well as
applications combining social and sensor networks

(as for example provided by (Breslin and Decker,
2007; Breslin et al., 2009) and (Miluzzo et al., 2007).
Recently, the benefits of social networking and
internet‐of‐things deployments for smart cities have
also been demonstrated in the context of a range of
EC co‐funded projects (Hernández-Muñoz et al.,
2011; Sanchez, 2010).
Current Smart City Data Analysis implies a wide
set of activities aiming to turn into actionable data the
outcome of complex analytics processes. This
analysis comprises among others: i) analysis of
thousands of traffic, pollution, weather, waste, energy
and event sensory data to provide better services to
the citizens, ii) event and incident analysis using near
real-time data collected by citizens and devices
sensors, iii) turning social media data related to city
issues into event and sentiment analysis , and many

others. Combining data from physical
(sensors/devices) and social sources (social
networks) can give more complete, complementary
data and contributes to better analysis and insights. In
overall, smart cities are complex social systems and
large scale data analytics can contribute into their
sustainability, efficient operation and welfare of the
citizens.
Motivated by the modern challenges in smart
cities, the RADICAL approach (RADICAL, 2016)
opens new horizons in the development, deployment
and operation of interoperable social networking and
Internet of Things services in smart cities, notably
services that could be flexibly and successfully
customized and replicated across multiple cities. Its
main goal is to provide the means for cities and SMEs
to rapidly develop, deploy, replicate, and evaluate a
diverse set of sustainable ICT services that leverage

established IoT and SN infrastructures. Application
services deployed and pilotedinvolve: i) Cycling
Safety Improvement, ii) Products Carbon Footprint
Management, iii) Object‐driven Data Journalism, iv)
Participatory Urbanism, v) Augmented Reality, vi)
Eco‐ consciousness, vii) Sound map of a city and viii)
City-R-Us: a crowdsourcing app for collecting
movement information using citizens smartphones.
The RADICAL platform is an open platform
having as added value the capability to easily
replicate the services in other smart cities, the ability
to co-design services with the involvement of cities'
Living Labs, and the use of added value services that
deal with the application development, the
sustainability analysis and the governance of the
services.
The RADICAL approach emphasizes on the
sustainability of the services deployed, targeting both
environmental sustainability and business viability.

Relevant indicators (e.g., CO2 emissions, Citizens
Satisfaction) are established and monitored as part of
the platform evaluation.End users (citizens) in
modern smart cities are increasingly looking for
media‐rich services offered under different space‐,
context‐, and situational conditions. The active
participation and interaction of citizens can be a key
enabler for successful and sustainable service
deployments in future cities. Social networks hold the
promise to boost such participation and interaction,
thereby boosting participatory connected governance
within the cities. However, in order to enable smart
cities get insight information on how citizens think,
act and talk about their city it is important to
understand their opinion and sentiment polarity on
issues related to their city context. This is where
sentiment analysis can play a significant role. As
social media data bring in significant Big Data
challenges (especially for unstructured data streams)

it will be important to find effective ways to analyse
sentimentally those data for extracting value
information and within specific time windows.
This paper has the following contributions:
uniform social and IoT big data aggregation
and combination.
t Analysis
techniques efficiency, to reduce record,
retrieval, update and processing time.
-grams storage and
frequency representation in the context of big
data Sentiment Analysis.
The rest of the paper is structured as follows:
Section 2 gives an overview of related and similar
works that can be found in the international literature
and in projects funded by the European Commission.
Section 3 presents the RADICAL architecture and
approach. Section 4 presents details about the
Sentiment Analysis problem and related experiments,

while in section 5 we provide the future work to be
planned in the context of RADICAL and the
conclusions we have come into.
2 RELATED WORK
Recently, various analytical services such as
sentiment analysis found their way into Internet of
Things (IoT) applications. With the devices that are
able to convey human messages over the internet
meeting an exponential growth, the challenge now
revolves around big data issues. Traditional
approaches do not cope with the requirements posed
from applications for analytics in e.g. high velocity
rates or data volumes. As a result, the integration of
IoT with social sensor data put common tasks like
feature extraction, algorithm training or model
updating to the test.
Most of the algorithms are memory-resident and
assume a small data size (He et al., 2010) and once
this threshold is exceeded, the algorithms’ accuracy
and performance degrades to the point they are
useless. Therefore even if we focused solely on
volume challenges, it is intuitively expected that the
accuracy of the supervised algorithms will be
affected. An attempt from (Liu et al., 2013) to use
Naïve Bayes in an increasingly large data volume,
showed that a rapid fall of the algorithms accuracy is
followed by a continuous, smooth increase

asymptotically tending from the lower end to the
baseline (best accuracy under normal data load).
Rather than testing the algorithm’s limitations,
most of the other approaches are focusing on
implementing parallel and distributed versions of the
algorithms such as (He et al., 2010; Read et al., 2015).
In fact most of them rely on the Map-Reduce
framework so as to achieve high throughput
classification (Amati et al., 2014; Sakaki et al., 2013;
Wang et al., 2012; Zhao et al., 2012) whereas a
number of toolkits have been presented with
implementations of distributed or parallel versions of
machine learning algorithms such as (“Apache
Mahout: Scalable machine learning and data mining,”
n.d., “MEKA: A Multi-label Extension to WEKA,”
n.d.; Bifet et al., 2010). While these solutions put
most of the emphasis in the model and the
optimization of the classification task in terms of
accuracy and throughput, there is a rather small body
of research dealing with the problem of feature
extraction in high pace streams. The standard solution
that is considered is the use of a sliding window and
the application of standard feature extraction
techniques in this small set. In cases where the
stream’s distribution is variable, a sliding window
kappa-based measure has been proposed (Bifet and
Frank, 2010).
As reported in (Strohbach et al., 2015), another
domain of intense research in the area of scalable
analytics is for an architecture that combines both
batch and stream processing over social and IoT data
while at the same time considering a single model for
different types of documents (e.g. tweets Vs

blogposts). Sentiment analysis is a typical task that
requires batch modeling in order to generate the
golden standards for each of the classes. This process
is also the most computationally intense, as the
classification task itself is usually a CPU bound task
(i.e. run the classification function). In a data
streaming scenario the golden standards must be
updated in a batch mode, whereas the feature
extraction and classification must take place in real
time.
Perhaps the most prominent example of such an
architecture is the Lambda Architecture (Marz and
Warren, 2015) pattern which solves the problem of
computing arbitrary functions on arbitrary data in
realtime by combining a batch layer for processing
large scale historical data and a streaming layer for
processing items being retrieved in real time from an
input queue or analytics in e.g. high velocity rates or
data volumes. As a result, the integration of IoT with
social sensor data put common tasks like feature
extraction, algorithm training or model updating to
the test.Most of the algorithms are memory-resident
and assume a small data size (He et al., 2010) and
once this threshold is exceeded, the algorithms’
accuracy and performance degrades to the point they
are useless. Therefore even if we focused solely on
volume challenges, it is intuitively expected that the
accuracy of the supervised algorithms will be
affected. An attempt from Liu et al (Liu et al., 2013)
to use Naïve Bayes in an increasingly large data
volume, showed that a rapid fall of the algorithms
accuracy is followed by a continuous, smooth
increase asymptotically tending from the lower end to
the baseline (best accuracy under normal data load).

3. THE RADICAL APPROACH
The RADICAL platform integrates components
and tools from (SocIoS, 2013) and (SmartSantander,
2013) projects, in order to support innovative smart
city services, leveraging information stemming from
Social Networks (SN) and Internet of Things devices.
Using the aforementioned tools, it can collect,
combine, analyze, process, visualize and provide
uniform access to big datasets of Social Network
content (e.g. tweets) and Internet of Things
information (e.g. sensor measurements or citizen
smartphone reports).
The architecture of the RADICAL platform is
depicted in Figure 1. As can be observed, all IoT data
are pushed into the platform through the respective
Application Programming Interfaces (IoT API and
Repository API) and are forwarded to the RADICAL
Repository, comprised by a MySQL database, formed

based on the RADICAL Object Model. The device-
related data, as dictated by this object model, are
saved in the form of Observations and Measurements.
Observations correspond to general IoT events
reported (e.g. a sensor report or bicycle "check-in"
event), while Measurements to more specific metrics
included in an Observation (e.g. Ozone
measurements (mpcc) or bicycle current speed
(km/h)). On the other hand, SN data are accessed in
real time from the underlying SN adaptors, by
communicating with the respective Networks’ APIs.
In cases of Social Networks like Foursquare that
provide plain venues and statistics, the adaptor-like
data structures do not make sense, thus relevant
Social Enablers are used to retrieve venue-related
information data.
On top of the main platform, RADICAL delivers
a set of tools (Application Management layer) that

allow end users to make better use of the RADICAL
platform, such as configuring the registered IoT
devices or extracting general activity statistics,
through the RADICAL Configuration API. Lastly,
the RADICAL Data API allows smart city services to
access the different sources of information (social
networks, IoT infrastructures, city applications),
combine data and perform data analysis by using the
appropriate platform tools.
Figure 1: RADICAL Platform Architecture
As can be seen in the Service Application Layer,
in the context of RADICAL a wide range of Smart
City services of various scopes has been developed:
izen journalism and Participatory
Urbanism: Those two interrelated services
allow citizens reporting events ofinterest in the

city, by posting images, text and metadata
through their smartphones.
sensors can report the situation in the city
streets through their smartphones.
products, people and services: By using a
range of sensors, the CO2 emissions in specific
places in a city may be monitored.
d Reality in Points of Interest
(POI): Tourists use their smartphones to
identify and receive information about points
of interest in a city.
-consciousness:Leverages
on the viral effect in the propagation of
information in the social networks as well as
the recycling policy of a city, through
monitoring and reporting relevant actions on
citizens' smartphones.
-Orientated Urban Noise Decibel

Measurement Application: Noise sensors are
employed throughout the city and citizens are
able to report and comment noise-related
information through SNs under a hashtag.
Urban Services: This service gathers sensory
data along with SN check-ins in city venues, to
construct a traffic map throughout the city,
leveraging the process load of anycentralized
decision making process.
The aforementioned services are piloted in six
European participating cities: Aarhus, Athens,
Genoa, Issy les Moulineaux, Santander and the region
of Cantabria. Figure 2illustrates a screenshot example
of the RADICAL Cities' Dashboard, where general
statistics on device registration and activity for a
service throughout different cities in a specific time
RADICAL
Data

Repository
City
Applications
Radical Data API
Service Application Layer
Sound MAPP
Municipality
Cockpit
City R-US
Service
aggregator
Repository
Gateway
Application Management Tools
Governance
Toolkit
Application
Development
Toolkit
Sustainability
services
Resource

Directory
Service
Storer
IoT Manager Event Broker
Node
Manager
Data Rep.
Configurator
Radical Configuration API
IoT devices
Repository API
Configuration Tools
Social Media
…
Register
Manager
IOT API SN Adaptors
SocIoS Core Service
SN Enablers
Event
Detection

Sentiment
Analysis
GWFPSens GW4IoT Devices GW4Serv…
Platform Tools
Carbon
Footprint
Augmented
Reality
Eco-
Conciousness
Object Driven
Journalism
Participatory
Urbanism
Cycling
Safety
Social Networks -
Venues Services
period is provided. In overall, during the last pilot

iteration, RADICAL Repository had captured a total
of 5.636 active IoT devices sending 728.253
Observations and 5.461.776 Measurements.
Most of the services above depend on the
aggregation of those IoTdata with social data
stemming from online Social Network sites. E.g. in
the Participatory Urbanism service, citizens' reports
sent through smartphones and saved in RADICAL
Repository are combined with relevant tweets (under
a city service hashtag), as well as POI information
that can be collected from similar SNs.
Figure 2: RADICAL Cities Dashboard presents smartphone
registrations and measurements for the AR service in the cities
of Santander and Cantabria over a period
Thus, given the size of the datasets acquired by
smart city services, along with the rich social media
content that can be retrieved through the RADICAL

platform adaptors, big data aggregation and analysis
challenges arise. Data Analysis tools are the ones that
further process the data in order to provide
meaningful results to the end user, i.e. Event
Detection or Sentiment Analysis.
When it comes to Big Data, as in the RADICAL
case, where millions of user-reported events are
aggregated along with millions of SN posts and an
extraction of general results is required, the challenge
accrued is two-fold: First, the tool must ensure the
accuracy of the analysis, in the sense that data
classification is correct to a certain and satisfactory
extent, and second, processing time must be kept
under certain limits, so that results retrieval process
delay is tolerable by end-users. Moreover, it is
apparent in such analysis that a trade-off between
effectiveness and efficiency exists. The latter is a
most crucial issue in Big Data analysis and apart from

the policy followed in data querying (e.g. for queries
preformed in an SQL database), it is also related to
the algorithmic techniques employed foranalyzing
those datasets.
In the context of this work, we focus on the
Sentiment Analysis on the big IoT and SN related
datasets of RADICAL, as this was the most popular
functionality among participating cities and almost all
of the RADICAL Smart City services presented
above make use of it. The goal of the Sentiment
Analysis service is to extract sentiment expressive
patterns from user-generated content in social
networks or IoT-originated text posts. The service
comes to the aid of the RADICAL city administrators,
helping them to categorize polarized posts, meaning
sentimentally charged text, e.g. analyse citizens’
posts to separate subjective from objective opinions
or count the overall positive and negative feedback,

concerning a specific topic or event in the city.
4. SENTIMENT ANALYSIS
EXPERIMENTS AND
PERFORMANCE IMPROVEMENT
4.1 Introduction
The term Sentiment Analysis refers to an
automatic classification problem. Its techniques are
trying to distinguish between sentences of natural
language conveying positive (e.g. happiness, pride,
joy), negative (e.g. anger, sadness, jealously) or even
neutral (no sentiment texts like statements, news,
reports)emotion (called sentiment for our purposes)
(Pang et al., 2002).
A human being is capable of understanding a
great variety of emotions from textual data. This
process of understanding is based on complicated
learning procedures that we all go through while
using our language as a means of communication, be
it actively or passively. It requires imagination and
subjectivity in order to fully understand the meaning
and hidden connections of each word in a sentence,
two things that machines lack.
The most common practice is to extract numerical
features out of the natural language (Godbole et al.,
2007). This process translates this complex means of
communication into something the machine can
process.

4.2 Natural Language Processing
In order to process the natural language data, the
computer has to take some pre-processing actions.
These actions include the cleansing of irrelevant,
erroneous or redundant data and the transformation of
the remaining data in a form more easily processed.
Cleansing the data has become a subjective task,
depending on the purposes of each researcher and the
chosen machine learning algorithms. The
transformation of the sentences in another form now
is clearly studied and each approach has some
advantages and disadvantages. This paper will detail
three approaches, two widely used and one that had
some success in improving the accuracy of the
algorithms: the bag of word, N-Gramsand N-Gram
Graphs(Aisopos et al., 2012; Fan and Khademi, 2014;
Giannakopoulos et al., 2008; Pang and Lee, 2008).
The bag of words approach is perhaps the most
simple and common one. It regards each sentence as
a set of words, disregarding their grammatical
connections and neighbouring relations. It splits each
sentence based on the space character (in most
languages) and then forms a set of unrelated words (a
bag of words as it is commonly called). Then each
word in this bag can be disregarded or rated by a
numerical value, in order to create a set of numbers
instead of words.
The N-Grams are a bit more complex. They also
form a bag of words but now each sentence is split
into pseudo-words of equal length. A sliding window

of N characters is rolling on the sentence creating this
bag of pseudo-words. For example if N=3 the
sentence “This is a nice weather we have today!” will
be split in the bag {‘Thi’, ‘his’, ‘is ’, ‘s i’, ‘ is’, ‘is ’,
‘s a’, ‘ a ’, ‘a n’, ‘ ni’, ‘nic’, ‘ice’, ‘ce ’, ‘e w’, ‘ we’,
‘wea’, ‘eat’, ‘ath’, ‘the’, ‘her’, ‘er ’, ‘r w’, ‘ we’, ‘we
’, ‘e h’, ‘ ha’, ‘hav’, ‘ave’, ‘ve ’, ‘e t’, ‘ to’, ‘tod’,
‘oda’, ‘day’, ‘ay!’}.
This technique takes into regard the direct
neighbouring relations by creating a continuous
stream of words, it still ignores the indirect relations
between words and even the relations between the
produced N-Grams. Of course it is impossible to have
a predefined set of numerical ratings for each one of
these pseudo-words because each sentence and each
N number (which is defined arbitrarily by the
researcher) produces a different set of pseudo-
words(Psomakelis et al., 2014). So machine learning
is commonly used to replace these words with
numerical values and create sets of numbers which
can be aggregated to sentence level.
An improvement on that approach aims to take
into consideration the neighbouring relations between
the produced N-Grams. This approach is called N-
Gram Graphs and its main concept is to create a graph
connecting each N-Gram with its neighbours in the
original sentence. So each node in this graph is an N-
Gram and each edge is a neighbouring
relation(Giannakopoulos et al., 2008). This approach
gives a variety of new informationto the researchers
and to the machine learning algorithms, including
information about the context of words, making it a
clear improvement of the simple N-Grams(Aisopos et
al., 2012). The only drawbacks are the complexity it

adds to the process and the difficulties of storing,
accessing and updating a graph of textual data.
4.3 Dataset Improvements
At the core of sentiment analysis is its dataset. We
are gathering and employing bigger and bigger
datasets in order to better train the algorithms to
distinguish what is positive and what is negative.
Classic storage techniques are proving more and more
cumbersome for large datasets. ArrayLists and most
Collections are adding a big overhead to the data so
they are not only enlarging the space requirements for
its storage but they are also delaying the analysis
process. So new techniques for data storage and
retrieval are needed, techniques that will enable us to
store even bigger datasets and access them with even
smaller delays.
The most commonly used such technique is the

Hash List(Fan and Khademi, 2014), which first
hashes the data in a certain, predefined amount of
buckets and then creates a List in each bucket to
resolve any collisions. This method’s performance is
heavily dependent on the quality of the hash function
and its ability to equally split the data into the buckets.
The target is to have as small lists as possible. That is
the case because finding the right bucket for a certain
piece of data is done in O(1) time but looking through
the List in that bucket for the correct spot to store the
piece of data is done in O(n) time where n is the
number of data pieces in the List.
Moreover, in Java which is the programming
language that we are using, each List is an object
containing one object for each data piece. All these
objects create an overhead that is not to be ignored. In
detail the estimated size that a hash list will occupy is
calculated as:

12 + ((B − E) ∗ 12) + (E ∗ 4)
+ (U ∗ (N ∗ 2 + 72))
Equation 1: Size estimation of Hash List where N=NGram
Length, U=Unique NGrams, B=Bucket Size, E=Empty
Buckets.
The worst case for storage but best for access time
is when almost each data piece has its own bucket. In
this case, for N=5, S=11881376, U=S, B=(26^N)*2,
E=11914220, we have a storage size of 1110 MB. The
best case for storage but worse for access time is when
all data pieces are in a small number of buckets, in big
lists. In this case for N=5, S=11881376, U=1,
B=(26^N)/2, E=200610 we have a storage size of 23
MB. In an average case of N=5, S=11881376,
U=7510766, B=26^N, E=2679046 we have 682 MB
of storage space needed. The sample for the above
examples was the complete range of 5-Grams for the
26 lowercase English characters which are 26^5 =

11881376.
Our proposed technique now, the one that we call
Dimensional Mapping, has a standard storage space,
depending only on the length of the N-Grams. The
idea is to store only the weight of each N-Gram with
the N-Gram itself being the pointer to where it is
stored. That is achieved by creating an N-dimensional
array of integers where each character of the N-Gram
is used as an index. So, in order to access the weight
of the 5-Gram ‘fdsgh’ in the table DM we would just
read the value in cell DM[‘f’][‘d’][‘s’][‘g’][‘h’]. A
very simple mapping is used between the characters
and an integers: after a very strict cleansing process
where we convert all characters in lowercase and
discard all characters but the 26 in the English
alphabet, we are just subtracting the ASCII value of
‘a’. Due to the serial nature of the characters that
gives us an integer between 0 and 26 that we can use

as an index. A more complex mapping can be used in
order to include more characters or even punctuation
that we now ignore.
The Dimensional Mapping has a standard storage
size requirement, dependent only on the length of the
N-Grams as we mentioned before. The size it
occupies can be estimated by the following formula:
(26� ) ∗ 4 + ∑ ((26�−� ) ∗ 12)
�=�
�=1
Equation 2: Dimensional Mapping size estimation with N
being the length of N-Grams.
This may seem large but for the 5-Grams the
estimated size is just 51 MB. Compared to the worst
case of Hash Lists (1110 MB) or even the average
case (682 MB) it seems like a huge improvement.
This is caused due to the fact that the

multidimensional array stores …
1
ELEG-548: Low Power VLSI Circuit Design, Spring 2020, Final
Exam
Name: ____________________________________ Student
ID: ______________________
Note: Use very brief answer for each question. Your grade is
decided by the accuracy instead of the
length of your answer.
1. (30’) A State Transition Graph (STG) for a sequential circuit
is shown in Figure 1. We need to
assign the states for four states S1, S2, S3 and S4 with two state
registers.
Figure 1. State transition graph (STG) of a sequential circuit
1). Assume all primary inputs patterns (AB=00, 01, 10, 11) are
equally probable. Based on STG,
find out conditional transition probabilities for all the
transitions, and mark them in the STG.
2). Assume the state probabilities of states are:
P(S1)=1/12, P(S2)=1/6, P(S3)=5/12, P(S4)=1/3.

Find the absolute transition probabilities Paij for all transitions
between states Si and Sj (i≠j), and
mark them in the STG.
3). Based on the results, find the weights Wij for all the
transitions between states Si and Sj (i≠j).
4). Based on the weighted STG, can you find out a low power
state assignment for the above
sequential circuit with minimum power cost function (Fc)
value? Please clearly show how you
achieve the low power state assignment step by step. Please
show your state assignment matrix,
and clearly mark the state assignment for each state (S1=? S2=?
S3=? S4=?).
5). For your low power state assignment, find out the power
cost function value Fc=?
2. (25’) A 16-bit carry-select full adder circuit is shown in
Figure 2. It takes two 16-bit inputs A, B:
A=A15A14…A0, B=B15B14…B0, and computes: SUM=A+B.
The carry-select full adder uses two 8-bit full adders to
precompute the sum of higher eight bits of
inputs assuming the carry-out from the sum of lower eight bits
to be “0” or “1” separately. The

actual carry-out from the lower eight bits will decide which
higher 8-bit SUM to be passed to the
final output (SUM<8:15>).
(1). This circuit can be revised to implement type-2 logic shut-
down technique to save power. The
A7, B7 bits can be used to construct predictor functions. Derive
the “1”-predictor function g1 and
“0”-predictor function g2 for the carryout of lower 8-bit full
adder. Whenever the carryout of lower
8-bit full adder can be predicted, one of the two higher 8-bit
full adders can be shut down to save
power.
2
(2). Re-plot the circuit separately to show how type-2 logic
shut-down technique is implemented.
You need to show clearly how g1 and g2 are connected in the
circuit. (Hint: You may need to add
LE (load_enable) for some registers.)
(3). Assume 1-probabilities p(A<i>)=0.25, p(B<i>)=0.82,
(i=0~15) what is the probabilities when
the circuit can be in shut-down mode? Assume average power of
each one-bit full adder as Padder,

the power of 8-bit full-adder block is 8Padder. If we only
consider the power consumption of
full-adders, while ignoring power consumption of other
gates/registers/Load_Enables, what would
be the approximate percentage power saving we can achieve by
using type-2 logic shut-down
technique (just a rough estimation)?
Figure 2. 16-bit carry-select full adder circuit
3. (25’) Low Power Data Communication: An 8-bit data bus is
driven by an 8-bit counter, as
shown in Figure 3.
1). If a regular binary counter is used, what is the average
number of bit switches per clock cycle
on the 8-bit data bus? Derive the equation to find out the
average number of bit switches per clock
cycle for N-bit binary counter. Show your proof of the equation
step by step.
2). If a gray-code counter is used, what is the average number
of bit switches per clock cycle on
the 8-bit data bus? What is the number of bit switches per clock
cycle for N-bit gray code counter?

3). If bus-inversion encoding technique is used to reduce the
power of data communication, sketch
how you are going to implement the bus-inversion encoding
logic (circuit design) on the 8-bit data
bus. For what patterns will the data be inverted? What is the
average number of bit switches per
clock cycle on the 8-bit data bus with bus-inversion encoding
technique?
4). Based on above 3 choices, which option gives the lowest
power consumption?
3
Figure 3. Data communication: 8-bit counter driving an 8-bit
data bus
4.(20’) A transmission gate based one-bit full adder circuit is
shown in Figure 4.
1). Based on the circuit design, what are the logic functions
implemented at P, S and Co? Derive its
logic expression, and use Boolean transformation to prove it
does implement the correct function
of Sum and Carry-out for one-bit full adder as below.

2). For this circuit in Figure 4, how are you going to measure its
power consumption using PSPICE
power simulation? Draw the complete circuit with auxiliary
power measurement circuitry and
clearly show where you are going to insert dummy voltage
sources.
Figure 4. A transmission gate based full adder
Due on 05/05/2020 (Tuesday) online in Canvas before 5pm.
Analysis on the Demand of Top Talent Introduction
in Big Data and Cloud Computing Field in China
Based on 3-F Method
Zhao Linjia, Huang Yuanxi, Wang Yinqiu, Liu Jia
National Academy of Innovation Strategy, China Association
for Science and Technology, Beijing, P.R.China
Abstract—Big data and cloud computing, which can help
China to implement innovation-driven development strategy and
promote industrial transformation and upgrading, is a new and
emerging industrial field in China. Educated, productive and
healthy workforces are necessary factor to develop big data and
cloud computing industry, especially top talents are essential.
Therefore, a three-step method named 3-F has been introduced

to help describing the distribution of top talents globally and
making decision whether they are needed in China. The 3-F
method relies on calculating the brain gain index to analysis the
top talent introduction demand of a country. Firstly, Focus on
the
high-frequency keywords of a specific field by retrieving the
highly cited papers. Secondly, using those keywords to Find out
the top talents of this specific field in the Web of Science.
Finally,
Figure out the brain gain index to estimate whether a country
need to introduce top talents of a specific field abroad. The
result
showed that the brain gain index value of China's big data and
cloud computing field was 2.61, which means China need to
introduce top talents abroad. Besides P. R. China, those top
talents mainly distributed in the United States, the United
Kingdom, Germany, Netherlands and France.
I. INTRODUCTION
Big data and cloud computing is a new and emerging
industrial field[1], and increasing widely used in China[2-4].
Talents’ experience is a source of technological mastery[5],
essentially for developing and using big data technologies.
Most European states consider the immigration of foreign
workers as an important factor to decelerate the decline of
national workforces[6]. Lots of universities and research
institutes have set up undergraduate and/or postgraduate
courses on data analytics for cultivating talents[7]. EMC
corporation think that vision, talent, and technology are
necessary elements to providing solutions to big data
management and analysis, insuring the big data success[8].
Bibliometrics research has appeared as early as 1917[9],
and has been proved an effective method for assessing or
identifying talents. Based on analyses of publication volume,

journals and their impact factors, most cited articles and
authors, preferred methods, and represented countries,
Gallardo-Gallardo et. al[10] assess whether talent management
should be approached as an embryonic, growth, or mature
phenomenon.
In this paper, we intend to analysis whether China need to
introduce top talents in the field of big data and cloud
computing by using bibliometrics. In section 2, the 3-F method
for top talent introduction demand analysis will be discussed.
In section 3, we will analysis the demand of top talent
introduction in big data and cloud computing field in China.
II. METHOD
In general, metering indicators contain the most productive
authors, journals, institutions, and countries, and the
collaboration networks between authors and institutions[11,
12]. Based on the commonly used bibliometrics method, 3-F
method for top talent introduction demand analysis is proposed.
3-F method has three steps:
Firstly, searching the literature database and forming a
high-impact literature collection in a specific field. Focusing on
the high-frequency keywords in the high-impact literature
collection by using the text analysis method as the research
hotspots. Just to be clear, the high-impact literature refers to the
journal literature whose number of cited papers ranked in the
top 1% in the same discipline and in the same year.
Secondly, retrieving those keywords in the Web of Science
to find out where those top talents of this specific field are.
Find the top talents by collected the information about talents’
country distribution, the institutions distribution and so on
through the high-impact literature collection. Among them, the

top talent refers to the first author or the communication author
of the high-impact literatures.
Finlly, Figure out the brain gain index to determine the top
talents introduction demand of a certain country. The brain gain
index is calculated as following formulas:
Iik = (Twk / Tik) / (Pw / Pi) (1)
Among them, Iik means the brain gain index value of
country (i) in the field (k), Twk means the number of world’s
top
talents in the field (k), Tik means the number of country’s (i)
top
talents in the field (k), Pw means the world population, Pi
means the country’s (i) population. If Iik was more than 1, that
means the country (i) has less top talents in the field (k),
therefore the talent introduction demand will be relatively
strong. In contrast, if Iik was less than 1, that means the
country’s (i) has greater top talents in the field (k) than the
world average, and the talent introduction demand will not be
so strong.
Additionally, the literature information mainly from the ISI
Web of Science (SCI, CPCI-S), and the the data analysis and
visualization tools are TDA and Tableau.
2017 Proceedings of PICMET '17: Technology Management for
Interconnected World
978-1-890843-36-6 ©2017 PICMET
III. CASE STUDY

Using 3-F method to analysis the top talents introduction
demand in the big data and cloud computing field. We
collected the high-impact literatures from January 1, 2006 to
July 31, 2016. The literature Language was English and the
literature type was article. Combining with the above
conditions, we got 546 high-impact literatures in the big data
and cloud computing field. Then the high-frequency keywords
have been obtained (Table 1) and served as the research
hotspots set.
TABLE I. THE RESEARCH HOTSPOTS OF THE HIGH-
IMPACT LITERATURES IN
BIG DATA AND CLOUD COMPUTING FIELD
Order Keywords Frequency
1 cloud computing 48
2 big data 24
3 virtualization 11
4 cloud manufacturing 9
5 internet of things (IoT) 8
6 mobile cloud computing 8
7 bioinformatics 6
8 climate change 6
9 Hadoop 6
10 software-defined networking (SDN) 6

……
At the same time, we displayed the frequency distribution
of research hotspots in the way of cloud chart(fig. 1).
Fig. 1. The cloud chart of research hotspots that in the field of
big data and
cloud computing
Then, we find the information about nationality (Table 2),
institutes (Table 3) of top talents in the high-impact literature
collection. Results showed there were 662 top talents
worldwide in the big data and cloud computing field. The top
ten countries or regions who had the most top talents were the
United States, P.R.China, the United Kindom, Germany, the
Netherlands, France, Canada, Australia, Italy and Switzerland
and Spain tied for the tenth.
TABLE II. THE NATIONALITY DISTRIBUTION OF TOP
TALENTS IN THE BIG
DATA AND CLOUD COMPUTING FIELD
Order Country or Region Number of the top talent
1 US 268
2 P. R. China 48
3 UK 47
4 Germany 39
5 Netherlands 28
6 France 27
7 Canada 22
8 Australia 21
9 Italy 19
10 Switzerland 13

Spain 13
12 Japan 10
13 Korea 8
Malaysia 8
15 Singapore 7
New Zealand 7
17 Austria 6
18 Belgium 5
Sweden 5
India 5
Chinese Taipei 5
……
TABLE III. THE INSTITUTES DISTRIBUTION OF TOP
TALENTS IN THE BIG
1 Harvard University (US) 10
2 Purdue University (US) 7
University of Malaya (Malaysia) 7
University of Maryland (US) 7
Unversity of Melbourne (Australia) 7
University of Missouri (US) 7
7 Oxford Unversity (UK) 6
8 Chinese Academy of Sciences (P.R.China) 5
ETH Zurich (Switzerland) 5

Massachusetts General Hospital (US) 5
Northwestern University (US) 5
University of British Columbia (Canada) 5
UC, Berkeley (US) 5
UC, San Diego (US) 5
University of Texas at Austin (US) 5
University of Washington (US) 5
……
From table 2 and 3 we can see that China was in the second
place worldwide. However, China's top talent is much less than
the United States. In addition, the overall strength of Chinese
research institutions is not strong. So, whether China should
introduce top talents from other countries is need to be
discussed.
According to the formula of the brain gain index, and using
the world population data as well as the Chinese mainland
population data released by the World Bank, the value of the
Chinese brain gain index of big data and cloud computing was
2.61. In comparison, the brain gain index value of the United
States was 0.11. That means China need to introduce top talent

in the field of big data and cloud computing.
IV. CONCLUSION
In the knowledge economy era, the international flow of top
talent has become convenient and frequent. Facing the world's
top talent shortage, China and the world's major countries have
developed overseas top talent introduction programs. Until
2007, almost all European countries had introduced some
skillselective migration policies in order to attract the top
talents. To make the overseas top talent introduction programs
more effective and targeted is helpful for occupying the
strategic high ground in the global top talent competition.
This paper improved the traditional talent evaluation
function of bibliometric method, and presented the 3-F analysis
method, which was applied to analyze the demand of top
talents. The 3F method could help the government official to
make decision whether need to introduce top talents to develop
a new industry field and lock these top talents geographic
location.
REFERENCES
[1] .Xu, B.M., X.G. Ni. Development Trend and Key Technical
Progress of
Cloud Computing[J]. Bulletin of the Chinese Academy of
Sciences,
2015. 30(2), pp. 170-180.
[2] Xiao, Y., Y. Cheng, Y.J. Fang, Research on Cloud
Computing and Its
Application in Big Data Processing of Railway Passenger Flow,
in
Iaeds15: International Conference in Applied Engineering and
Management, P. Ren, Y. Li, and H. Song, Editors. 2015, Aidic

Servizi
Srl: Milano. pp. 325-330.
[3] Zhu, Y.Q., P. Luo, Y.Y. Huo et. al, Study on Impact and
Reform of Big
Data on Higher Education in China, in 2015 3rd International
Conference on Social Science and Humanity, G. Lee and Y. Wu,
Editors. 2015, Information Engineering Research Inst, USA:
Newark. p.
155-161.
[4] Wang, X., L.C. Song, G.F. Wang et.al. Operational Climate
Prediction
in the Era of Big Data in China: Reviews and Prospects[J].
Journal of
Meteorological Research, 2016. 30(3), pp. 444-456.
[5] Dahlman, C., L. Westphal, Technological effort in industrial
development——An Interpretative Survey of Recent
Research[R]. 1982.
[6] Cerna, L., M. Czaika, European Policies to Attract Talent:
The Crisis
and Highly Skilled Migration Policy Changes, in High-Skill
Migration
and Recession. 2016, Springer. pp. 22-43.
[7] Jin, X., B.W. Wah, X. Cheng et. al. Significance and
challenges of big
data research[J]. Big Data Research, 2015. 2(2), pp. 59-64.
[8] Fang, H., Z. Zhang, C.J. Wang et. al. A survey of big data
research[J].
IEEE Network, 2015. 29(5), pp. 6-9.
[9] Cole, F.J., Eales, N. B. The history of comparative

anatomy[J]. science
Progress, 1917. 11, pp. 578-596.
[10] Gallardo-Gallardo, E., S. Nijs, N. Dries et. al. Towards an
understanding
of talent management as a phenomenon-driven field using
bibliometric
and content analysis[J]. Human Resource Management Review,
2015.
25, pp. 264-279.
[11] Clarke, B.L. Multiple authorship trends in scientific
papers[J]. Science,
1964. 143(3608), pp. 822-824.
[12] Gonzalez-Valiente, C.L., J. Pacheco-Mendoza, R.
Arencibia-Jorge. A
review of altmetrics as an emerging discipline for research
evaluation[J].
Learned Publishing, 2016. 29(4), pp. 229-238.
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Analysis on the Demand of Top Talent Introduction
in Big Data and Cloud Computing Field in China

Based on 3-F Method
Zhao Linjia, Huang Yuanxi, Wang Yinqiu, Liu Jia
National Academy of Innovation Strategy, China Association
for Science and Technology, Beijing, P.R.China
Abstract—Big data and cloud computing, which can help
China to implement innovation-driven development strategy and
promote industrial transformation and upgrading, is a new and
emerging industrial field in China. Educated, productive and
healthy workforces are necessary factor to develop big data and
cloud computing industry, especially top talents are essential.
Therefore, a three-step method named 3-F has been introduced
to help describing the distribution of top talents globally and
making decision whether they are needed in China. The 3-F
method relies on calculating the brain gain index to analysis the
top talent introduction demand of a country. Firstly, Focus on
the
high-frequency keywords of a specific field by retrieving the
highly cited papers. Secondly, using those keywords to Find out
the top talents of this specific field in the Web of Science.
Finally,
Figure out the brain gain index to estimate whether a country
need to introduce top talents of a specific field abroad. The
result
showed that the brain gain index value of China's big data and
cloud computing field was 2.61, which means China need to
introduce top talents abroad. Besides P. R. China, those top
talents mainly distributed in the United States, the United
Kingdom, Germany, Netherlands and France.
I. INTRODUCTION
Big data and cloud computing is a new and emerging

industrial field[1], and increasing widely used in China[2-4].
Talents’ experience is a source of technological mastery[5],
essentially for developing and using big data technologies.
Most European states consider the immigration of foreign
workers as an important factor to decelerate the decline of
national workforces[6]. Lots of universities and research
institutes have set up undergraduate and/or postgraduate
courses on data analytics for cultivating talents[7]. EMC
corporation think that vision, talent, and technology are
necessary elements to providing solutions to big data
management and analysis, insuring the big data success[8].
Bibliometrics research has appeared as early as 1917[9],
and has been proved an effective method for assessing or
identifying talents. Based on analyses of publication volume,
journals and their impact factors, most cited articles and
authors, preferred methods, and represented countries,
Gallardo-Gallardo et. al[10] assess whether talent management
should be approached as an embryonic, growth, or mature
phenomenon.
In this paper, we intend to analysis whether China need to
computing by using bibliometrics. In section 2, the 3-F method
for top talent introduction demand analysis will be discussed.
In section 3, we will analysis the demand of top talent
introduction in big data and cloud computing field in China.
II. METHOD
In general, metering indicators contain the most productive
authors, journals, institutions, and countries, and the
collaboration networks between authors and institutions[11,
12]. Based on the commonly used bibliometrics method, 3-F

method for top talent introduction demand analysis is proposed.
3-F method has three steps:
Firstly, searching the literature database and forming a
high-impact literature collection in a specific field. Focusing on
the high-frequency keywords in the high-impact literature
collection by using the text analysis method as the research
hotspots. Just to be clear, the high-impact literature refers to the
journal literature whose number of cited papers ranked in the
top 1% in the same discipline and in the same year.
Secondly, retrieving those keywords in the Web of Science
to find out where those top talents of this specific field are.
Find the top talents by collected the information about talents’
country distribution, the institutions distribution and so on
through the high-impact literature collection. Among them, the
top talent refers to the first author or the communication author
of the high-impact literatures.
Finlly, Figure out the brain gain index to determine the top
talents introduction demand of a certain country. The brain gain
index is calculated as following formulas:
Iik = (Twk / Tik) / (Pw / Pi) (1)
Among them, Iik means the brain gain index value of
country (i) in the field (k), Twk means the number of world’s
top
talents in the field (k), Tik means the number of country’s (i)
top
talents in the field (k), Pw means the world population, Pi
means the country’s (i) population. If Iik was more than 1, that
means the country (i) has less top talents in the field (k),
therefore the talent introduction demand will be relatively
strong. In contrast, if Iik was less than 1, that means the
country’s (i) has greater top talents in the field (k) than the

world average, and the talent introduction demand will not be
so strong.
Additionally, the literature information mainly from the ISI
Web of Science (SCI, CPCI-S), and the the data analysis and
visualization tools are TDA and Tableau.
978-1-890843-36-6 ©2017 PICMET
III. CASE STUDY
Using 3-F method to analysis the top talents introduction
demand in the big data and cloud computing field. We
collected the high-impact literatures from January 1, 2006 to
July 31, 2016. The literature Language was English and the
literature type was article. Combining with the above
conditions, we got 546 high-impact literatures in the big data
and cloud computing field. Then the high-frequency keywords
have been obtained (Table 1) and served as the research
hotspots set.
TABLE I. THE RESEARCH HOTSPOTS OF THE HIGH-
IMPACT LITERATURES IN
BIG DATA AND CLOUD COMPUTING FIELD
Order Keywords Frequency
1 cloud computing 48
2 big data 24

3 virtualization 11
4 cloud manufacturing 9
5 internet of things (IoT) 8
6 mobile cloud computing 8
7 bioinformatics 6
8 climate change 6
9 Hadoop 6
10 software-defined networking (SDN) 6
……
At the same time, we displayed the frequency distribution
of research hotspots in the way of cloud chart(fig. 1).
Fig. 1. The cloud chart of research hotspots that in the field of
big data and
cloud computing
Then, we find the information about nationality (Table 2),
institutes (Table 3) of top talents in the high-impact literature
collection. Results showed there were 662 top talents
worldwide in the big data and cloud computing field. The top
ten countries or regions who had the most top talents were the
United States, P.R.China, the United Kindom, Germany, the
Netherlands, France, Canada, Australia, Italy and Switzerland
and Spain tied for the tenth.

TABLE II. THE NATIONALITY DISTRIBUTION OF TOP
TALENTS IN THE BIG
1 US 268
2 P. R. China 48
3 UK 47
4 Germany 39
5 Netherlands 28
6 France 27
7 Canada 22
8 Australia 21
9 Italy 19
10 Switzerland 13
Spain 13
12 Japan 10
13 Korea 8
Malaysia 8
15 Singapore 7
New Zealand 7
17 Austria 6
18 Belgium 5
Sweden 5
India 5
Chinese Taipei 5
……
TABLE III. THE INSTITUTES DISTRIBUTION OF TOP
TALENTS IN THE BIG
1 Harvard University (US) 10

2 Purdue University (US) 7
University of Malaya (Malaysia) 7
University of Maryland (US) 7
Unversity of Melbourne (Australia) 7
University of Missouri (US) 7
7 Oxford Unversity (UK) 6
8 Chinese Academy of Sciences (P.R.China) 5
ETH Zurich (Switzerland) 5
Massachusetts General Hospital (US) 5
Northwestern University (US) 5
University of British Columbia (Canada) 5
UC, Berkeley (US) 5
UC, San Diego (US) 5
University of Texas at Austin (US) 5
University of Washington (US) 5
……

From table 2 and 3 we can see that China was in the second
place worldwide. However, China's top talent is much less than
the United States. In addition, the overall strength of Chinese
research institutions is not strong. So, whether China should
introduce top talents from other countries is need to be
discussed.
According to the formula of the brain gain index, and using
the world population data as well as the Chinese mainland
population data released by the World Bank, the value of the
Chinese brain gain index of big data and cloud computing was
2.61. In comparison, the brain gain index value of the United
States was 0.11. That means China need to introduce top talent
in the field of big data and cloud computing.
IV. CONCLUSION
In the knowledge economy era, the international flow of top
talent has become convenient and frequent. Facing the world's
top talent shortage, China and the world's major countries have
developed overseas top talent introduction programs. Until
2007, almost all European countries had introduced some
skillselective migration policies in order to attract the top
talents. To make the overseas top talent introduction programs
more effective and targeted is helpful for occupying the
strategic high ground in the global top talent competition.
This paper improved the traditional talent evaluation
function of bibliometric method, and presented the 3-F analysis
method, which was applied to analyze the demand of top
talents. The 3F method could help the government official to
make decision whether need to introduce top talents to develop
a new industry field and lock these top talents geographic

location.
REFERENCES
[1] .Xu, B.M., X.G. Ni. Development Trend and Key Technical
Progress of
Cloud Computing[J]. Bulletin of the Chinese Academy of
Sciences,
2015. 30(2), pp. 170-180.
[2] Xiao, Y., Y. Cheng, Y.J. Fang, Research on Cloud
Computing and Its
Application in Big Data Processing of Railway Passenger Flow,
in
Iaeds15: International Conference in Applied Engineering and
Management, P. Ren, Y. Li, and H. Song, Editors. 2015, Aidic
Servizi
Srl: Milano. pp. 325-330.
[3] Zhu, Y.Q., P. Luo, Y.Y. Huo et. al, Study on Impact and
Reform of Big
Data on Higher Education in China, in 2015 3rd International
Conference on Social Science and Humanity, G. Lee and Y. Wu,
Editors. 2015, Information Engineering Research Inst, USA:
Newark. p.
155-161.
[4] Wang, X., L.C. Song, G.F. Wang et.al. Operational Climate
Prediction
in the Era of Big Data in China: Reviews and Prospects[J].
Journal of
Meteorological Research, 2016. 30(3), pp. 444-456.
[5] Dahlman, C., L. Westphal, Technological effort in industrial
development——An Interpretative Survey of Recent
Research[R]. 1982.

[6] Cerna, L., M. Czaika, European Policies to Attract Talent:
The Crisis
and Highly Skilled Migration Policy Changes, in High-Skill
Migration
and Recession. 2016, Springer. pp. 22-43.
[7] Jin, X., B.W. Wah, X. Cheng et. al. Significance and
challenges of big
data research[J]. Big Data Research, 2015. 2(2), pp. 59-64.
[8] Fang, H., Z. Zhang, C.J. Wang et. al. A survey of big data
research[J].
IEEE Network, 2015. 29(5), pp. 6-9.
[9] Cole, F.J., Eales, N. B. The history of comparative
anatomy[J]. science
Progress, 1917. 11, pp. 578-596.
[10] Gallardo-Gallardo, E., S. Nijs, N. Dries et. al. Towards an
understanding
of talent management as a phenomenon-driven field using
bibliometric
and content analysis[J]. Human Resource Management Review,
2015.
25, pp. 264-279.
[11] Clarke, B.L. Multiple authorship trends in scientific
papers[J]. Science,
1964. 143(3608), pp. 822-824.
[12] Gonzalez-Valiente, C.L., J. Pacheco-Mendoza, R.
Arencibia-Jorge. A
review of altmetrics as an emerging discipline for research
evaluation[J].
Learned Publishing, 2016. 29(4), pp. 229-238.

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Research Paper - Data Science and Big Data Analytics
This week's reading centered around how Big Data analytics can
be used with Smart Cities. This is exciting and can provide
many benefits to individuals as well as organizations. For this
week's research assignment, you are to search the Internet for
other uses of Big Data in RADICAL platforms. Please pick an
organization or two and discuss the usage of big data in
RADICAL platforms including how big data analytics is used in
those situations as well as with Smart Cities. Be sure to use the
UC Library for scholarly research. Google Scholar is the 2nd
best option to use for research.
Your paper should meet the following requirements:
• Be approximately 3-5 pages in length, not including the
required cover page and reference page.
• Follow APA guidelines. Your paper should include an
introduction, a body with fully developed content, and a
conclusion.
• Support your response with the readings from the course and
at least five peer-reviewed articles or scholarly journals to
support your positions, claims, and observations. The UC
Library is a great place to find resources.
• Be clear with well-written, concise, using excellent grammar
and style techniques. You are being graded in part on the

quality of your writing.
References:
L. Zhao, Y. Huang, Y. Wang and J. Liu, "Analysis on the
Demand of Top Talent Introduction in Big Data and Cloud
Computing Field in China Based on 3-F Method," 2017 Portland
International Conference on Management of Engineering and
Technology (PICMET), Portland, OR, 2017, pp. 1-3. doi:
10.23919/PICMET.2017.8125463
Discussion 1 – Data Science and Big Data Analytics
In this week's reading, the concept of 3-F Method is introduced.
Discuss the purpose of this concept and how it is calculated.
Also perform your own research/analysis using these factors and
provide your assessment on whether the United States need to
computing by using bibliometrics.
Please make your initial post and two response posts
substantive. A substantive post will do at least TWO of the
following:
Ask an interesting, thoughtful question pertaining to the topic
Answer a question (in detail) posted by another student or the
instructor
Provide extensive additional information on the topic
Explain, define, or analyze the topic in detail
Share an applicable personal experience
Provide an outside source (for example, an article from the UC
Library) that applies to the topic, along with additional
information about the topic or the source (please cite properly
in APA)
Make an argument concerning the topic.
At least one scholarly source should be used in the initial
discussion thread. Be sure to use information from your

readings and other sources from the UC Library. Use proper
citations and references in your post.
References:
L. Zhao, Y. Huang, Y. Wang and J. Liu, "Analysis on the
Demand of Top Talent Introduction in Big Data and Cloud
Computing Field in China Based on 3-F Method," 2017 Portland
International Conference on Management of Engineering and
Technology (PICMET), Portland, OR, 2017, pp. 1-3. doi:
10.23919/PICMET.2017.8125463
Psomakelis, E., Aisopos, F., Litke, A., Tserpes, K., Kardara,
M., & Campo, P.M. (2016). Big IoT and Social Networking
Data for Smart Cities - Algorithmic Improvements on Big Data
Analysis in the Context of RADICAL City Applications.
CLOSER.
Discussion 2 – Information Governance
Chapter 1 : The Onslaught of Big Data and the Information
Governance Imperative
Organizations are struggling to reduce and right-size their
information foot-print, using data governance techniques like
data cleansing and de-duplication. Why is this effort
necessary?
Requirements:
· APA Format
· 300 – 400 Words
· 2 Scholarly References
TextBook:
Smallwood, R. F. (2014). Information Governance : Concepts,
Strategies, and Best Practices. Wiley. ISBN: 9781118218303

BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docx

BIG IOT AND SOCIAL NETWORKING DATA FOR SMART CITIES Alg.docx

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