scholarly journals A Domain Specific Entity Linking Approach Consuming Multistore Environment

2018 ◽  
pp. 46-52
Author(s):  
Emrah Inan ◽  
Burak Yonyul ◽  
Fatih Tekbacak
Keyword(s):  
Big Data ◽  
Data Models ◽  
Use Cases ◽  
Unstructured Data ◽  
Entity Linking ◽  
Domain Specific ◽  
Storage Technology ◽  
Integrated Environment ◽  
Data Environment ◽  
The Web

Most of the data on the web is non-structural, and it is required that the data should be transformed into a machine operable structure. Therefore, it is appropriate to convert the unstructured data into a structured form according to the requirements and to store those data in different data models by considering use cases. As requirements and their types increase, it fails using one approach to perform on all. Thus, it is not suitable to use a single storage technology to carry out all storage requirements. Managing stores with various type of schemas in a joint and an integrated manner is named as 'multistore' and 'polystore' in the database literature. In this paper, Entity Linking task is leveraged to transform texts into wellformed data and this data is managed by an integrated environment of different data models. Finally, this integrated big data environment will be queried and be examined by presenting the method.

Storage Infrastructure for Big Data and Cloud

2014 ◽  
pp. 110-128 ◽  
Author(s):  
Anupama C. Raman
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Storage ◽  
Unstructured Data ◽  
Storage Area Networks ◽  
Storage Technology ◽  
Object Based ◽  
Storage Area ◽  
Storage Technologies ◽  
Big Data Storage

Unstructured data is growing exponentially. Present day storage infrastructures like Storage Area Networks and Network Attached Storage are not very suitable for storing huge volumes of unstructured data. This has led to the development of new types of storage technologies like object-based storage. Huge amounts of both structured and unstructured data that needs to be made available in real time for analytical insights is referred to as Big Data. On account of the distinct nature of big data, the storage infrastructures for storing big data should possess some specific features. In this chapter, the authors examine the various storage technology options that are available nowadays and their suitability for storing big data. This chapter also provides a bird's eye view of cloud storage technology, which is used widely for big data storage.

Research on the organization of user needs information in the big data environment

2017 ◽  
Vol 35 (1) ◽  
pp. 36-49
Author(s):  
Feicheng Ma ◽  
Ye Chen ◽  
Yiming Zhao
Keyword(s):  
Big Data ◽  
Conceptual Model ◽  
Semantic Knowledge ◽  
User Needs ◽  
User Privacy ◽  
Content Type ◽  
Knowledge Resources ◽  
Individual Privacy ◽  
Data Environment ◽  
The Web

Purpose This paper aims to propose a conceptual model for improving the organization of user needs information in the big data environment. Design/methodology/approach A conceptual model of the organization of user needs information based on Linked Data techniques is constructed. This model has three layers: the Data Layer, the Semantic Layer and the Application Layer. Findings Requirements for organizing user needs information in the big data environment are identified as follows: improving the intelligence level, establishing standards and guidelines for the description of user needs information, enabling the interconnection of user needs information and considering individual privacy in the organization and analysis of user needs. Practical implications This Web of Needs model could be used to improve knowledge services by matching user needs information with increasing semantic knowledge resources more effectively and efficiently in the big data environment. Originality/value This study proposes a conceptual model, the Web of Needs model, to organize and interconnect user needs. Compared with existing methods, the Web of Needs model satisfies the requirements for the organization of user needs information in the big data environment with regard to four aspects: providing the basis and conditions for intelligent processing of user needs information, using RDF as a description norm, enabling the interconnection of user needs information and setting various protocols to protect user privacy.

scholarly journals Recuperação da Informação em Ambientes Semânticos: uma ferramenta aplicada à publicações científicas

2015 ◽  
Vol 1 (1) ◽  
pp. 30 ◽  
Author(s):  
Caio Saraiva Coneglian ◽  
Elvis Fusco
Keyword(s):  
Social Media ◽  
Big Data ◽  
Information Retrieval ◽  
Great Difficulty ◽  
Unstructured Data ◽  
Added Value ◽  
Informational Needs ◽  
Retrieval Process ◽  
Extraction Agent ◽  
The Web

The data available on the Web is growing exponentially, providing information of high added value to organizations. Such information can be arranged in diverse bases and in varied formats, like videos and photos in social media. However, unstructured data present great difficulty for the information retrieval, not efficiently meeting the informational needs of the users, because there are problems in understanding the meaning of documents stored on the Web. In the context of an Information Retrieval architecture, this research aims to The implementation of a semantic extraction agent in the context of the Web that allows the location, treatment and retrieval of information in the context of Big Data in the most varied informational sources that serves as the basis for the implementation of informational environments that aid the Information Retrieval process , Using ontology to add semantics to the process of retrieval and presentation of results obtained to users, thus being able to meet their needs.

Sentiment Mining Approaches for Big Data Classification and Clustering

2018 ◽  
pp. 34-63
Author(s):  
Ashok Kumar J ◽  
Abirami S ◽  
Tina Esther Trueman
Keyword(s):  
Big Data ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Real World ◽  
Unstructured Data ◽  
Real World Problem ◽  
Sentiment Mining ◽  
Big Data Classification ◽  
The Web ◽  
Classification And Clustering

Sentiment analysis is one of the most important applications in the field of text mining. It computes people's opinions, comments, posts, reviews, evaluations, and emotions which are expressed on products, sales, services, individuals, organizations, etc. Nowadays, large amounts of structured and unstructured data are being produced on the web. The categorizing and grouping of these data become a real-world problem. In this chapter, the authors address the current research in this field, issues and the problem of sentiment analysis on Big Data for classification and clustering. It suggests new methods, applications, algorithm extensions of classification and clustering and software tools in the field of sentiment analysis.

scholarly journals Comparison of NoSQL Database and Traditional Database-An emphatic analysis

2018 ◽  
Vol 2 (2) ◽  
pp. 51
Author(s):  
M. Sandeep Kumar ◽  
Prabhu .J
Keyword(s):  
Big Data ◽  
Web Application ◽  
Unstructured Data ◽  
Cost Comparison ◽  
Huge Amount ◽  
Relation Database ◽  
Complex Queries ◽  
Huge Data ◽  
Nosql Database ◽  
The Web

A Huge amount of data is manipulated by using the web application, Facebook, Twitter, social sites etc. Most of the data are unstructured data. It is not desirable for storing, performing and analyzing data in the relational database for huge data. It affords way towards performing NoSQL database and uses fully for handling the big data. In this paper, we present the performance in store and query operation in NoSQL database, estimating the performance of both reads and write operation using simple and complex queries. Result represents that comparing Cassandra with relation database, Cassandra outperforms the relation database. Most of the organization used only Hbase and Cassandra for benefit of cost. Comparison Various NoSQL Database, issues while performing NoSQL database. 

Big Data Environment and Humanities Platform

2017 ◽  
Vol 39 (5) ◽  
pp. 177-202
Author(s):  
Hyun-Cheol Choi
Keyword(s):  
Big Data ◽  
Data Environment
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