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Sliding windows over uncertain data streams.

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Title: Sliding windows over uncertain data streams.
Authors: Dallachiesa, Michele michele.dallachiesa@gmail.com, Jacques-Silva, Gabriela1 g.jacques@us.ibm.com, Gedik, Buğra2 bgedik@cs.bilkent.edu.tr, Wu, Kun-Lung1 klwu@us.ibm.com, Palpanas, Themis themis@mi.parisdescartes.fr
Source: Knowledge & Information Systems. Oct2015, Vol. 45 Issue 1, p159-190. 32p.
Subjects: Existentially closed groups, Unit construction, Search algorithms, Windows (Graphical user interfaces), Uncertainty (Information theory)
Abstract: Uncertain data streams can have tuples with both value and existential uncertainty. A tuple has value uncertainty when it can assume multiple possible values. A tuple is existentially uncertain when the sum of the probabilities of its possible values is $$<$$ 1. A situation where existential uncertainty can arise is when applying relational operators to streams with value uncertainty. Several prior works have focused on querying and mining data streams with both value and existential uncertainty. However, none of them have studied, in depth, the implications of existential uncertainty on sliding window processing, even though it naturally arises when processing uncertain data. In this work, we study the challenges arising from existential uncertainty, more specifically the management of count-based sliding windows, which are a basic building block of stream processing applications. We extend the semantics of sliding window to define the novel concept of uncertain sliding windows and provide both exact and approximate algorithms for managing windows under existential uncertainty. We also show how current state-of-the-art techniques for answering similarity join queries can be easily adapted to be used with uncertain sliding windows. We evaluate our proposed techniques under a variety of configurations using real data. The results show that the algorithms used to maintain uncertain sliding windows can efficiently operate while providing a high-quality approximation in query answering. In addition, we show that sort-based similarity join algorithms can perform better than index-based techniques (on 17 real datasets) when the number of possible values per tuple is low, as in many real-world applications. [ABSTRACT FROM AUTHOR]
Copyright of Knowledge & Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: &lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Dallachiesa%2C+Michele%22&quot;&gt;Dallachiesa, Michele&lt;/searchLink&gt;&lt;i&gt; michele.dallachiesa@gmail.com&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Jacques-Silva%2C+Gabriela%22&quot;&gt;Jacques-Silva, Gabriela&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;i&gt; g.jacques@us.ibm.com&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Gedik%2C+Buğra%22&quot;&gt;Gedik, Buğra&lt;/searchLink&gt;&lt;relatesTo&gt;2&lt;/relatesTo&gt;&lt;i&gt; bgedik@cs.bilkent.edu.tr&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Wu%2C+Kun-Lung%22&quot;&gt;Wu, Kun-Lung&lt;/searchLink&gt;&lt;relatesTo&gt;1&lt;/relatesTo&gt;&lt;i&gt; klwu@us.ibm.com&lt;/i&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;AR&quot; term=&quot;%22Palpanas%2C+Themis%22&quot;&gt;Palpanas, Themis&lt;/searchLink&gt;&lt;i&gt; themis@mi.parisdescartes.fr&lt;/i&gt;
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  Data: &lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Existentially+closed+groups%22&quot;&gt;Existentially closed groups&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Unit+construction%22&quot;&gt;Unit construction&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Search+algorithms%22&quot;&gt;Search algorithms&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Windows+%28Graphical+user+interfaces%29%22&quot;&gt;Windows (Graphical user interfaces)&lt;/searchLink&gt;&lt;br /&gt;&lt;searchLink fieldCode=&quot;DE&quot; term=&quot;%22Uncertainty+%28Information+theory%29%22&quot;&gt;Uncertainty (Information theory)&lt;/searchLink&gt;
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  Data: Uncertain data streams can have tuples with both value and existential uncertainty. A tuple has value uncertainty when it can assume multiple possible values. A tuple is existentially uncertain when the sum of the probabilities of its possible values is $$&lt;$$ 1. A situation where existential uncertainty can arise is when applying relational operators to streams with value uncertainty. Several prior works have focused on querying and mining data streams with both value and existential uncertainty. However, none of them have studied, in depth, the implications of existential uncertainty on sliding window processing, even though it naturally arises when processing uncertain data. In this work, we study the challenges arising from existential uncertainty, more specifically the management of count-based sliding windows, which are a basic building block of stream processing applications. We extend the semantics of sliding window to define the novel concept of uncertain sliding windows and provide both exact and approximate algorithms for managing windows under existential uncertainty. We also show how current state-of-the-art techniques for answering similarity join queries can be easily adapted to be used with uncertain sliding windows. We evaluate our proposed techniques under a variety of configurations using real data. The results show that the algorithms used to maintain uncertain sliding windows can efficiently operate while providing a high-quality approximation in query answering. In addition, we show that sort-based similarity join algorithms can perform better than index-based techniques (on 17 real datasets) when the number of possible values per tuple is low, as in many real-world applications. [ABSTRACT FROM AUTHOR]
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  Data: &lt;i&gt;Copyright of Knowledge &amp; Information Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Type: general
      – SubjectFull: Unit construction
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      – SubjectFull: Search algorithms
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