By Charu C. Aggarwal (auth.), Charu C. Aggarwal (eds.)
In fresh years, the development in expertise has made it attainable for organisations to shop and checklist huge streams of transactional info. Such info units which always and quickly develop over the years are often called information streams.
Data Streams: types and Algorithms basically discusses concerns regarding the mining elements of knowledge streams instead of the database administration point of streams. This quantity covers mining elements of knowledge streams in a complete variety. each one contributed bankruptcy, from quite a few renowned researchers within the facts mining box, includes a survey at the subject, the most important principles within the box from that specific subject, and destiny learn directions.
Data Streams: types and Algorithms is meant for a certified viewers composed of researchers and practitioners in undefined. This booklet can be acceptable for graduate-level scholars in computing device science.
Charu C. Aggarwal received his B.Tech in machine technological know-how from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a examine employees Member at IBM considering that then, and has released over ninety papers in significant meetings and journals within the database and information mining box. He has utilized for, or been granted, over 50 US and foreign patents, and has two times been distinct grasp Inventor at IBM for the industrial price of his patents. He has been granted 14 invention success awards by way of IBM for his patents. His paintings on genuine time bio-terrorist possibility detection in information streams gained the IBM Epispire award for environmental excellence in 2003. He has served at the software committee of so much significant database meetings, and used to be application chair for the information Mining and data Discovery Workshop, 2003, and a application vice-chair for the SIAM convention on information Mining, 2007. he's an affiliate editor of the IEEE Transactions on information Engineering and an motion editor of the information Mining and data Discovery magazine. he's a senior member of the IEEE.
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Extra resources for Data Streams: Models and Algorithms
The methods have clear advantages over recent techniques which try to cluster the whole stream at one time rather than viewing the stream as a changing process over time. The CluStream model provides a wide variety of functionality in characterizing data stream clusters over different time horizons in an evolving environment. This is achieved through a careful division of labor between the online statistical data collection component and an offline analytical component. Thus, the process provides considerable flexibility to an analyst in a real-time and changing environment.
4 Let h be a user-specijied time window, and t, be the current time. Let us also assume that max-capacity such that h/2 5 t, - t, I:2 . h. > 2. Then a snapshot exists at time t,, Proof: Let r be the smallest integer such that h < 2T+1. Since r is the smallest such integer, it also means that h > 2'. This means that for any interval (t, - h, t,) of length h, at least one integer t' E (t, - h, t,) must exist which satisfies the property that t' mod 2'-l = 0 and t' mod 2r # 0. Let t' be the time stamp of the last (most current) such snapshot.
2, we get t, - t, 2' 2r h + h = 2 . h. Thus, we have: < + < < The above result ensures that every possible horizon can be closely approximated within a modest level of accuracy. While the geometric time frame shares a number of conceptual similarities with the pyramidal time frame , it is actually quite different and also much more efficient. This is because it eliminates the double counting of the snapshots over different frame numbers, as is the case with the pyramidal time frame . 2, we present an example of a frame table illustrating snapshots of different frame numbers.