Join queries on uncertain data: Semantics and efficient processing

Ge, Tingjian

doi:10.1109/icde.2011.5767888

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…These can be used for uncertain databases as well. Additionally, work has been done to perform join operation efficiently for locationally as well as existentially uncertain data in [12,17]. The problem of dependency between tuples in successive joins was explored by [4].…”

Section: Related Workmentioning

confidence: 99%

Probabilistic aggregate skyline join queries

Bhattacharya

Awate

2015

Proceedings of the 27th International Conference on Scientific and Statistical Database Management

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The multi-criteria decision making, made possible by the advent of skyline queries, has been successfully applied in many areas. Though most of the earlier work is concerned with only a single relation, several real world applications require finding the skyline set over multiple relations. Consequently, the join operation over skylines where the preferences are local to each relation and/or on aggregated values of attributes from different relations, has been proposed. In the meanwhile, uncertain datasets are witnessing increasing applications in many scientific and real-life situations. The problem of skyline computation for such datasets becomes even more challenging as every object can be classified as a skyline with some probability. In this paper, we introduce probabilistic aggregate skyline join queries (PASJQ) that ask for objects whose probability of being a skyline from a join of two uncertain relations is over a query probability threshold. The skyline preferences are on both local and aggregate attributes. Since the naïve algorithm can be impractical, we propose three algorithms to efficiently process such queries. The algorithms process the skylines as much as possible locally before computing the join to reduce the computation burden of finding skylines from the larger joined relation. Experiments with real and synthetic data exhibit the practicality and scalability of these algorithms with respect to query probability threshold, cardinality, dimensionality and other parameters of the uncertain relations.

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Section: Related Workmentioning

confidence: 99%

Probabilistic aggregate skyline join queries

Bhattacharya

Awate

2015

Proceedings of the 27th International Conference on Scientific and Statistical Database Management

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“…Of particular relevance to our work on position uncertainty is indexing and storing multi-dimensional uncertain data, including earlier work in ORION [10,13] and more recent work [41,26,20,31]. They can be leveraged in array databases as well, but can trigger many index I/O's (as we showed in §5) and may not be effective when the filtering power is low.…”

Section: Related Workmentioning

confidence: 99%

Supporting Data Uncertainty in Array Databases

Peng

Diao

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

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Uncertain data management has become crucial to scientific applications. Recently, array databases have gained popularity for scientific data processing due to performance benefits. In this paper, we address uncertain data management in array databases, which may involve both value uncertainty within individual tuples and position uncertainty regarding where a tuple should belong in an array given uncertain dimension attributes. Our work defines the formal semantics of array operations under both value and position uncertainty. To address the new challenge raised by position uncertainty, we propose a suite of storage and evaluation strategies for array operations, with a focus on a new scheme that bounds the overhead of querying by strategically treating tuples with large variances via replication in storage. Results from real datasets show that for common workloads, our best-performing techniques outperform alternative methods based on state-of-the-art indexes by 1.7x to 4.3x for the Subarray operation and 1 to 2 orders of magnitude for Structure-Join, at only a small storage cost.

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“…Boolean Queries: Boolean Queries are performed as they would be in an inverted index, using only the primary index to get the document lists and then merging them using a zigzag join algorithm [3,6,13]. Phrase Queries: The index is searched using a divide and conquer algorithm (see Fig.…”

Section: Searching the Indexmentioning

confidence: 99%

Triple Indexing: An Efficient Technique for Fast Phrase Query Evaluation

Gugnani¹,

Roul²

2014

IJCA

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Phrase query evaluation is an important task of every search engine. Optimizing the query evaluation time for phrase queries is the biggest threat for the current search engine. Usually, phrase queries are a hassle for standard indexing techniques. This is generally because, merging the posting lists and checking the word ordering takes a lot of time. This paper proposes a new technique called Triple Indexing to index web documents which optimizes query evaluation time for phrase queries by reducing the time for merging the posting lists and checking the word ordering. In addition, a proper procedure has been put forward for document ranking using an extended vector space model. The 4 Universities dataset and Industry Sector dataset of Carnegie Mellon University has been used for experimental purpose and it has been found that using the proposed method with a modern machine, the query time for phrase queries is reduced by almost 50 percent, compared to a standard inverted index.

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Join queries on uncertain data: Semantics and efficient processing

Cited by 7 publications

References 26 publications

Probabilistic aggregate skyline join queries

Probabilistic aggregate skyline join queries

Supporting Data Uncertainty in Array Databases

Triple Indexing: An Efficient Technique for Fast Phrase Query Evaluation

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