<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

Temporal Query Processig Using Sql Server

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Temporal Query Processig Using Sql Server"}

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...

Temporal Query Processig Using Sql Server

Online veröffentlicht: 01. Sept. 2017

Seitenbereich: 495 - 505

Eingereicht: 27. Mai 2017

Akzeptiert: 15. Juni 2017

DOI: https://doi.org/10.21307/ijssis-2017-266

© 2017 Mastan Vali Shaik et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.