Claremont Report on Database Research: Research Directions (Le Gruenwald)

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Ähnlich wie Claremont Report on Database Research: Research Directions (Le Gruenwald)

SciDB : Open Source Data Management System for Data-Intensive Scientific Anal...

The object-oriented database (OODB) is the combination of object-oriented programming language (OOPL) systems and persistent systems. Object DBMSs add database functionality to object programming languages. They bring much more than persistent storage of programming language objects. A major benefit of this approach is the unification of the application and database development into a seamless data model and language environment. This report presents the comparison between object oriented database and relational database. It gives advantages of OODBMS over RDBMS. It gives applications of OODBMS.

Comparison of Relational Database and Object Oriented Database

Editor IJMTER

Nowadays, the database field has gotten much more diverse, and as a result, a variety of non-relational (NoSQL) databases have been created, including JSON-document databases and key-value stores, as well as extensible markup language (XML) and graph databases. Due to the emergence of a new generation of data services, some of the problems associated with big data have been resolved. In addition, in the haste to address the challenges of big data, NoSQL abandoned several core databases features that make them extremely efficient and functional, for instance the global view, which enables users to access data regardless of how it is logically structured or physically stored in its sources. In this article, we propose a method that allows us to query non-relational databases based on the ontology-based access data (OBDA) framework by delegating SPARQL protocol and resource description framework (RDF) query language (SPARQL) queries from ontology to the NoSQL database. We applied the method on a popular database called Couchbase and we discussed the result obtained.

Bridging the gap between the semantic web and big data: answering SPARQL que...

IJECEIAES

The technology of object oriented databases was introduced to system developers in the late 1980’s. Object DBMSs add database functionality to object programming languages. A major benefit of this approach is the unification of the application and database development into a seamless data model and language environment. As a result, applications require less code, use more natural data modeling, and code bases are easier to maintain.

Survey of Object Oriented Database

Editor IJMTER

The web of data: how are we doing so far?

Elena Simperl

The Rise of Nosql Databases

JAMES NGONDO

En ch01

anibapi

The web of data: how are we doing so far

Chapter01

Ena ch01

Ena ch01

The aim of this paper is to evaluate, through indexing techniques, the performance of Neo4j and OrientDB, both graph databases technologies and to come up with strength and weaknesses os each technology as a candidate for a storage mechanism of a graph structure. An index is a data structure that makes the searching faster for a specific node in concern of graph databases. The referred data structure is habitually a B-tree, however, can be a hash table or some other logic structure as well. The pivotal point of having an index is to speed up search queries, primarily by reducing the number of nodes in a graph or table to be examined. Graphs and graph databases are more commonly associated with social networking or “graph search” style recommendations. Thus, these technologies remarkably are a core technology platform for some Internet giants like Hi5, Facebook, Google, Badoo, Twitter and LinkedIn. The key to understanding graph database systems, in the social networking context, is they give equal prominence to storing both the data (users, favorites) and the relationships between them (who liked what, who ‘follows’ whom, which post was liked the most, what is the shortest path to ‘reach’ who). By a suitable application case study, in case a Twitter social networking of almost 5,000 nodes imported in local servers (Neo4j and Orient-DB), one queried to retrieval the node with the searched data, first without index (full scan), and second with index, aiming at comparing the response time (statement query time) of the aforementioned graph databases and find out which of them has a better performance (the speed of data or information retrieval) and in which case. Thereof, the main results are presented in the section 6.

Evaluation of graph databases

ijaia

MIDESS

JISC CETIS

Introduction to question answering for linked data & big data

Andre Freitas

data base system to new data science lerne

tarunprajapati0t

For a same domain, several databases (DBs) exist. The emergence of classical web to the semantic web has contributed to the appearance of the notion of ontology that have shared and consensual vocabulary. For a given, it is more interesting to take advantage of existing databases, to build an ontology. Most of the data are already stored in these databases. So many DBs can be integrated to enable reuse of existing data for the semantic web. Even for existing ontologies, the relevance of the information they contain requires regular updating. These databases can be useful sources to enrich these ontologies. In the other hand, for these ontologies more than the ratio ‘size of the instances on the size of working memory’ is large more than the management of these instances, in memory, is difficult. Finding a way to store these instances in a structured manner to satisfy the needs of performance and reliability required for many applications becomes an obligation. As a consequence, defining query languages to support these structures becomes a challenge for SW community. We will show through this paper how ontologies can benefit from DBs to increase system performance and facilitate their design cycle. The DBs in their turn suffers from several drawbacks namely complexity of the design cycle and lack of semantics. Since ontologies are rich in semantic, DBs can profit from this advantage to overcoming their drawbacks.

USING ONTOLOGIES TO OVERCOMING DRAWBACKS OF DATABASES AND VICE VERSA: A SURVEY

cseij

Graph applications were once considered “exotic” and expensive. Until recently, few software engineers had much experience putting graphs to work. However, the use cases are now becoming more commonplace. This talk explores a practical use case, one which addresses key issues of data governance and reproducible research, and depends on sophisticated use of graph technology. Consider: some academic disciplines such as astronomy enjoy a wealth of data — mostly open data. Popular machine learning algorithms, open source Python libraries, and distributed systems all owe much to those disciplines and their history of big data. Other disciplines require strong guarantees for privacy and security. Datasets used in social science research involve confidential details about human subjects: medical histories, wages, home addresses for family members, police records, etc. Those cannot be shared openly, which impedes researchers from learning about related work by others. Reproducibility of research and the pace of science in general are limited. Nonetheless, social science research is vital for civil governance, especially for evidence-based policymaking (US federal law since 2018). Even when data may be too sensitive to share openly, often the metadata can be shared. Constructing knowledge graphs of metadata about datasets — along with metadata about authors, their published research, methods used, data providers, data stewards, and so on — that provides effective means to tackle hard problems in data governance. Knowledge graph work supports use cases such as entity linking, discovery and recommendations, axioms to infer about compliance, etc. This talk reviews the Rich Context AI competition and the related ADRF framework used now by more than 15 federal agencies in the US. We’ll explore knowledge graph use cases, use of open standards and open source, and how this enhances reproducible research. Social science research for the public sector has much in common with data use in industry. Issues of privacy, security, and compliance overlap, pointing toward what will be required of banks, media channels, etc., and what technologies apply. We’ll look at comparable work emerging in other parts of industry: open source projects, open standards emerging, and in particular a new set of features in Project Jupyter that support knowledge graphs about data governance.

Graph Realities

Connected Data World

NoSQL Basics - a quick tour

Bikram Sinha. MBA, PMP

(Big) Data (Science) Skills

Oscar Corcho

This webinar features project overviews of all EarthCube Awards (Building Blocks, Research Coordination Networks, Conceptual Designs, and Test Governance), followed by a call for involvement, and a Q&A session. Agenda: EarthCube Awards – Project Overviews 1.. EarthCube Web Services (Building Block) 2. EC3: Earth-Centered Community for Cyberinfrastructure (RCN) 3. GeoSoft (Building Block) 4. Specifying and Implementing ODSIP (Building Block) 5. A Broker Framework for Next Generation Geoscience (BCube) (Building Block) 6. Integrating Discrete and Continuous Data (Building Block) 7. EAGER: Collaborative Research (Building Block) 8. A Cognitive Computer Infrastructure for Geoscience (Building Block) 9. Earth System Bridge (Building Block) 10. CINERGI – Community Inventory of EC Resources for Geoscience Interoperability (BB) 11. Building a Sediment Experimentalist Network (RCN) 12. C4P: Collaboration and Cyberinfrastructure for Paleogeosciences (RCN) 13. Developing a Data-Oriented Human-centric Enterprise for Architecture (CD) 14. Enterprise Architecture for Transformative Research and Collaboration (CD) 15. EC Test Enterprise Governance: An Agile Approach (Test Governance) A Call for Involvement!

EarthCube Monthly Community Webinar- Nov. 22, 2013

EarthCube

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SciDB : Open Source Data Management System for Data-Intensive Scientific Anal...

Comparison of Relational Database and Object Oriented Database

Bridging the gap between the semantic web and big data: answering SPARQL que...

Survey of Object Oriented Database

The web of data: how are we doing so far?

The Rise of Nosql Databases

En ch01

The web of data: how are we doing so far

Chapter01

Ena ch01

Evaluation of graph databases

MIDESS

Introduction to question answering for linked data & big data

data base system to new data science lerne

USING ONTOLOGIES TO OVERCOMING DRAWBACKS OF DATABASES AND VICE VERSA: A SURVEY

Graph Realities

NoSQL Basics - a quick tour

(Big) Data (Science) Skills

EarthCube Monthly Community Webinar- Nov. 22, 2013

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