Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data

Cheng, Reynold; Xia, Yuni; Prabhakar, Sunil; Shah, Rahul; Vitter, Jeffrey Scott

doi:10.1016/b978-012088469-8.50077-2

Cited by 189 publications

(189 citation statements)

References 9 publications

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“…They suffer from loss of accuracy and efficiency. Parallel to this modeling effort, there has also been a lot of recent work on querying and indexing pdf attributes in databases [2], [3], [4], [5], [6], [7].…”

Section: Overhead Of Historiesmentioning

confidence: 99%

“…We have also studied indexing methods for attribute uncertainty, both for continuous [6] and categorical [7] distributions. Apart from our work, there has been other work by [2], [3], [5] on indexing pdfs.…”

Section: Overhead Of Historiesmentioning

confidence: 99%

“…One deals with modeling and the other with efficient execution of queries. Work on query processing over probabilistic data has assumed a simple model -a single (continuous or discrete) attribute that takes on probabilistic values [2], [3], [4], [5], [6], [7]. Most of this work is focussed on developing index structures for efficient query evaluation over probability distribution (or density) functions (pdf).…”

Section: Introductionmentioning

confidence: 99%

“…Recently proposed models for probabilistic relational data deal with the representation and management of tuple uncertainty (with the exception of [6]). These models are naturally well-suited for applications with categorical uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…The recently proposed models [9], [10], [11] generalize most of the earlier models for probabilistic relational data. In contrast, attribute uncertainty models [6], [12] consider that a tuple is definitely part of the database, but one or more of its attributes is (are) not known with certainty. The model in [6] allows an uncertain value to take on a continuous ranges of values, but all other work has been focussed on the case of discrete uncertainty (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Database Support for Probabilistic Attributes and Tuples

Singh¹,

Mayfield²,

Shah³

et al. 2008

2008 IEEE 24th International Conference on Data Engineering

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Abstract-The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle imprecise data at the database level. Uncertainty can be at the attribute or tuple level and is present in both continuous and discrete data domains. This paper presents a model for handling arbitrary probabilistic uncertain data (both discrete and continuous) natively at the database level. Our approach leads to a natural and efficient representation for probabilistic data. We develop a model that is consistent with possible worlds semantics and closed under basic relational operators. This is the first model that accurately and efficiently handles both continuous and discrete uncertainty. The model is implemented in a real database system (PostgreSQL) and the effectiveness and efficiency of our approach is validated experimentally.

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Section: Overhead Of Historiesmentioning

confidence: 99%

Section: Overhead Of Historiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Database Support for Probabilistic Attributes and Tuples

Singh¹,

Mayfield²,

Shah³

et al. 2008

2008 IEEE 24th International Conference on Data Engineering

Self Cite

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A survey on indexing techniques for mobility in Internet of Things': Challenges, performances, and perspectives

2020

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SummaryMobile nodes arouse new challenges that expand the performance in those environments. The nodes of Internet of Things (IoT) generate a large amount of data, which have to be stored and processed in a seamless and interpretable form by indexing. Therefore, indexing is a challenge in IoT, particularly when the nodes are mobile. The current indexing techniques dedicated to mobile environments are unsatisfying because the data are transmitted from different locations within different time periods and randomness of sensor movement. Although huge research efforts have been dedicated to this subject amid the last decades, little consideration has been paid for the research summarization and guidance. The objective of this survey is to identify the relationship between activities of mobile sensors in the context of IoT, that concerned on transferring and collecting data as well as the effectiveness of indexing techniques. The contribution of this review is to investigate the techniques of mobile IoT nodes, to find the source of challenges that adversely affect the index effectiveness. In‐depth investigation and analyses of approaches to apply IoT mobile nodes will enable the researcher to understand the behavior of mobile environments to extract the deformities that adversely affect the indexing effectiveness. The analyzed approaches are meta‐heuristic, spatial‐temporal indexing, and prediction model approach. Each approach is analyzed by discussing the features and limitations from the mobility perspectives. Furthermore, the indexing techniques are analyzed according to their features and limitations and mobile IoT indexing requirements. In conclusion, recognize and contemplate different open issues that got little focus or still unknown at this point.

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Probabilistic Spatial Queries on Existentially Uncertain Data

Dai

Yiu

Mamoulis

et al. 2005

Advances in Spatial and Temporal Databases

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Abstract.We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.

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Efficient Indexing Methods for Probabilistic Threshold Queries over Uncertain Data

Cited by 189 publications

References 9 publications

Database Support for Probabilistic Attributes and Tuples

Database Support for Probabilistic Attributes and Tuples

A survey on indexing techniques for mobility in Internet of Things': Challenges, performances, and perspectives

Probabilistic Spatial Queries on Existentially Uncertain Data

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