Ranking distributed probabilistic data

Li, Feifei; Yi, Ke; Jestes, Jeffrey

doi:10.1145/1559845.1559885

Cited by 53 publications

(32 citation statements)

References 39 publications

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“…in the contex of central uncertain databases shing &al [9] had proposed for shearshing and managing uncertain catÃ c gorical data tow index structure, the first one called probabilistic inverted index based on inverted liste and the second Probabilistic Distribution Rtree based on and Rtree structure, in our approach we have deal wiht the first Algorithm 2: Top-k Query Algorithm index structure. in the contex of distributed environement there has been many approach studied the topk query and rankin distriubuted uncertain databases [6,16,5,8],in the appraoche of Li&al [16] the tuples in local site are sorted based on expected rank, the query query are excuted from a central server that he accesses tuples from all site in order ranking then he maintains a priority queue to store each sites tuples with their ranks. Then it initializes the priority queue with first tuple from each site.…”

Section: Related Workmentioning

confidence: 99%

Indexing Uncertain Categorical Data over Distributed Environment

Benaissa¹,

Ouziri²,

Sahri³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Today, a large amount of uncertain data is produced by several applications where the management systems of traditional databases incuding indexing methods are not suitable to handle such type of data. In this paper, we propose an inverted based index method for effciently searching uncertain categorical data over distributed environments. We adress two kinds of query over the distributed uncertain databases, one a distributed probabilistic thresholds query, where all results sastisfying the query with probablities that meet a probablistic threshold requirement are returned, and another a distributed top k-queries, where all results optimizing the transfer of the tuples and the time treatment are returned.

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Section: Related Workmentioning

confidence: 99%

Indexing Uncertain Categorical Data over Distributed Environment

Benaissa¹,

Ouziri²,

Sahri³

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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“…distributed sensor networks and multiple data sources for information integration [10][11][12]. Unfortunately, existing techniques that include indexing and query processing over uncertain data were mainly proposed in centralized environments and are not adaptable to distributed environments.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, it is still challenging to efficiently process queries over distributed uncertain data. Notable exceptions include recent work on indexing and query processing of distributed uncertain data [10][11][12][13]. These works have only considered top-k queries on uncertain real-valued attributes.…”

Section: Introductionmentioning

confidence: 99%

Framework for Managing Uncertain Distributed Categorical Data

Benaissa¹,

Yahmi²,

Jamil³

2017

ijacsa

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Abstract-In recent years, data has become uncertain due to the flourishing advanced technologies that participate continuously and increasingly in producing large amounts of incomplete data. Often, many modern applications where uncertainty occurs are distributed in nature, e.g., distributed sensor networks, information extraction, data integration, social network, etc. Consequently, even though the data uncertainty has been studied in the past for centralized behavior, it is still a challenging issue to manage uncertainty over the data in situ. In this paper, we propose a framework to managing uncertain categorical data over distributed environments that is built upon a hierarchical indexing technique based on inverted index, and a distributed algorithm to efficiently process queries on uncertain data in distributed environment. Leveraging this indexing technique, we address two kinds of queries on the distributed uncertain databases 1) a distributed probabilistic thresholds query, where its answers satisfy the probabilistic threshold requirement; and 2) a distributed top-k-queries, optimizing, the transfer of the tuples from the distributed sources to the coordinator site and the time treatment. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method in terms of communication costs and response time.

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“…Several uncertain data models are proposed [2,12,4], and probabilistic ranking queries are studied [34,16,11,7,23,24] which are based on the interplay of score to be ranked and probability to be observed.…”

Section: Introductionmentioning

confidence: 99%

“…Cormode et al also propose to rank uncertain data based on their expected rank values [11], called expected rank semantic. Li et al study ranking distributed uncertain data based on the expected rank semantic [23]. The existing approaches assume that both scores based on which objects are Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic ranking over relations

Chang

Qin

et al. 2010

Proceedings of the 13th International Conference on Extending Database Technology

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Probabilistic top-k ranking queries have been extensively studied due to the fact that data obtained can be uncertain in many real applications. A probabilistic top-k ranking query ranks objects by the interplay of score and probability, with an implicit assumption that both scores based on which objects are ranked and probabilities of the existence of the objects are stored in the same relation. We observe that in general scores and probabilities are highly possible to be stored in different relations, for example, in column-oriented DBMSs and in data warehouses. In this paper we study probabilistic top-k ranking queries when scores and probabilities are stored in different relations. We focus on reducing the join cost in probabilistic top-k ranking. We investigate two probabilistic score functions, discuss the upper/lower bounds in random access and sequential access, and provide insights on the advantages and disadvantages of random/sequential access in terms of upper/lower bounds. We also propose random, sequential, and hybrid algorithms to conduct probabilistic top-k ranking. We conducted extensive performance studies using real and synthetic datasets, and report our findings in this paper.

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Ranking distributed probabilistic data

Cited by 53 publications

References 39 publications

Indexing Uncertain Categorical Data over Distributed Environment

Indexing Uncertain Categorical Data over Distributed Environment

Framework for Managing Uncertain Distributed Categorical Data

Probabilistic ranking over relations

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