TEMPORAL QUERY PROCESSIG USING SQL SERVER

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 10 , ISSUE 5 (December 2017) > List of articles

Special Issue

TEMPORAL QUERY PROCESSIG USING SQL SERVER

Mastan Vali Shaik * / P Sujatha

Keywords : temporal data, social networks, temporal database, data mining, Temporal query processing, time stamp using SQL Server.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 10, Issue 5, Pages 495-505, DOI: https://doi.org/10.21307/ijssis-2017-266

License : (CC BY-NC-ND 4.0)

Received Date : 27-May-2017 / Accepted: 15-June-2017 / Published Online: 01-September-2017

ARTICLE

ABSTRACT

Most data sources in real-life are not static but change their information in time. This evolution of data in time can give valuable insights to business analysts. Temporal data refers to data, where changes over time or temporal aspects play a central role. Temporal data denotes the evaluation of object characteristics over time. One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. Temporal queries on time evolving data are at the heart of a broad range of business and network intelligence applications ranging from consumer behaviour analysis, trend analysis, temporal pattern mining, and sentiment analysis on social media, cyber security, and network monitoring. Social networks (SN) such as Facebook, twitter, LinkedIn contains huge amount of temporal information. Social media forms a dynamic and evolving environment. Similar to real-world friendships, social media interactions evolve over time. People join or leave groups; groups expand, shrink, dissolve, or split over time. Studying the temporal behaviour of communities is necessary for a deep understanding of communities in social media(SM). In this paper we focus on the use of temporal data and temporal data mining in social networks.

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