Probabilistic n-of-N skyline computation over uncertain data streams

Zhang, Wenjie; Li, Aiping; Cheema, Muhammad Aamir; Zhang, Ying; Chang, Lijun

doi:10.1007/s11280-014-0292-2

Cited by 12 publications

(2 citation statements)

References 17 publications

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“…In the aspect of uncertain skyline queries, typically, Yang and Wang propose an uncertain n ‐of‐ N skyline query method which uses RDO tree, interval tree, and time‐label tree to optimize the update of data streams. Similarly, an effective pruning technique is proposed in the work of Zhang et al to minimize the number of uncertain elements to be kept, which also develop a novel encoding scheme together with update techniques to reduce the overhead.…”

Section: Related Workmentioning

confidence: 99%

Parallelizing uncertain skyline computation against n‐of‐N data streaming model

Liu

Ren

et al. 2018

Concurrency and Computation

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The skyline query over uncertain data streams, as an important aspect of big data analysis, plays a significant role in domains such as environment monitoring, decision-making, and data mining.The skyline query over uncertain data streams with sliding window model always focuses on the most recent N streaming items, which cannot meet the query requirements of different window scales at the same time. To improve the query flexibility and efficiency, we propose an efficient parallel method for processing uncertain n-of-N skyline queries; that is, computing the skyline for the most recent n (∀n ≤ N) items in parallel. Specifically, we first propose a framework for parallelizing the query computation for uncertain n-of-N skylines. Furthermore, we put forward a sliding window partitioning strategy as well as a streaming items mapping strategy to realize the load balance for each node. In addition, we define a spatial index structure RST based on R-tree to organize the elements within each individual sliding window and candidate set in each which can significantly improve the dominance tests. Most importantly, we provide an encoding interval scheme to transform the n-of-N query into stabbing query in each compute node, which can greatly minimize the query scope and improve the query efficiency. In addition, we use a red-black tree named RBI to store all stabbing intervals. Extensive experimental results demonstrate that the proposals are efficient and can greatly meet the query requirement of users in real applications. KEYWORDS data streams, n-of-N model, parallel queries, skyline queries, uncertain data INTRODUCTIONWith the fast development of computer technology and easily available network services, uncertain data query has received extensive attention in a large number of practical applications in domains like location-based service, 1 RFID network, 2 online shopping, 3 and radar detection. 4 Uncertain data is inherent in these applications due to various factors, 5 such as data randomness and incompletness, limited facilities of measuring, loss of data transmission, and interference of external environment. Moreover, uncertain data in these applications are often generated dynamically and continuously and gradually evolve into uncertain data streams. For example, in the online shopping applications, information of goods are usually updated continuously, and uncertain data such as the satisfaction scores from the feedback of the customers are collected from multiple web sites dynamically. As another example, in the application of resource detecting with radar detection and ranging, a large number of geological and oceanographic data generate continuously and are transmitted to the processing systems in real time. Therefore, it is greatly important to analyze large collections of uncertain streaming data efficiently, due to the significance of such real applications and characteristics of uncertain data streams such as real-time arriving, data uncertainty, and single-pass scanning.The skyline query is a typical que...

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Section: Related Workmentioning

confidence: 99%

Parallelizing uncertain skyline computation against n‐of‐N data streaming model

Liu

Ren

et al. 2018

Concurrency and Computation

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“…Skyline queries are more often used for filtering numerical data, as in research [3], [4], and [5]. Unlike other studies that use numerical data, research [7] uses categorical data, namely the Association of Computing Machinery Computing Classification System (ACM CCS) ontology and the ACM CCS literature.…”

Section: Introductionmentioning

confidence: 99%

Aggregate Functions in Categorical Data Skyline Search (CDSS) for Multi-keyword Document Search

Mardiah¹,

Annisa²,

Neyman³

2023

khif

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Literature review is the first step in starting research for a deep understanding of the research interest. However, finding literature relevant to research interests is difficult and takes time. Skyline query is a method that can be used for filtering. An object p is said to dominate object q if p equals q on all of its attributes, and p is at least better than q on one attribute. Categorical Data Skyline Search (CDSS) is an algorithm that can filter skyline objects in categorical data types such as documents. CDSS uses Extended Distance Wu and Palmer (DEWP) to calculate the distance between the user query and document keywords. The document keywords and user queries are represented as nodes in the ACM CCS ontology, and documents are assumed to be represented by a single keyword. This study aims to use the CDSS algorithm to search for skyline documents represented by more than one keyword by adding an aggregate function (average, minimum, maximum) to the CDSS algorithm, especially in calculating DEWP. This study used the thesis documents from the IPB University computer science department. Document keywords will be extracted using the Term Frequency-Inverse Term Frequency (TF-IDF) method. The collected keywords will be mapped in a mixed ontology tree that refers to the Association of Computing Machinery Computing Classification System 2012 (ACM CCS 2012) and Computer Science Ontology (CSO) as ontology standards in computer science. The skyline query algorithm for determining skyline documents is Block Nested Loop (BNL). The evaluation method uses the skyline ratio of each aggregate function in the CDSS. Based on the ratio value, CDSS using the maximum DEWP has the most relevant skyline results compared to the average DEWP and minimum DEWP.

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Parallel n-of-N Skyline Queries over Uncertain Data Streams

Liu

Ren

et al. 2018

Lecture Notes in Computer Science

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Probabilistic n-of-N skyline computation over uncertain data streams

Cited by 12 publications

References 17 publications

Parallelizing uncertain skyline computation against n‐of‐N data streaming model

Parallelizing uncertain skyline computation against n‐of‐N data streaming model

Aggregate Functions in Categorical Data Skyline Search (CDSS) for Multi-keyword Document Search

Parallel n-of-N Skyline Queries over Uncertain Data Streams

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