Reconciling skyline and ranking queries

Ciaccia, Paolo; Martinenghi, Davide

doi:10.14778/3137628.3137653

Cited by 39 publications

(54 citation statements)

References 25 publications

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“…Top-k queries are clearly a very simple, efficient and easy-to-use approach and this probably explains why it is widespread. In addition, the ability to control the size of the output and the trade-offs among attributes should not be underestimated [4,5,7,16]. However, this method is not free of drawbacks and can have substantial disadvantages.…”

Section: Pros and Consmentioning

confidence: 99%

“…Skylines completely solve the weights problem and this approach does not require them to work properly [4,5,7,9]. Actually, this approach is more intuitive since it adapts itself to the multi-criteria optimization problem while ranking queries transform the problem in a single-objective optimization one [9].…”

Section: Pros and Consmentioning

confidence: 99%

“…Problem 1 will be dealt with in this survey by showing some of the most widely recognized or innovative techniques. Multi-objective optimization in data sets is commonly obtained with three main approaches [4,7,9]:…”

Section: Introductionmentioning

confidence: 99%

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An outline of multi objective optimization in databases with focus on flexible skyline queries

Savino¹

2022

Preprint

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The problem of optimizing across different, conceivably conflicting, criteria is called multi-objective optimization and it is widely spread across many fields. This is a recurring problem in database queries when there is the need of obtaining the best objects from a very large data set. In this article, I included a complete review of the main approaches typically used to achieve multi-criteria optimization. Starting from ranking queries and skylines and then proceeding to more advanced methods, this paper aims to define a clear outline of multi-objective optimization in databases. In particular, the flexible skyline paradigm is considered and thoroughly discussed as it overcomes many of the critical issues that arise with other methods.

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Section: Pros and Consmentioning

confidence: 99%

Section: Pros and Consmentioning

confidence: 99%

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An outline of multi objective optimization in databases with focus on flexible skyline queries

Savino¹

2022

Preprint

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“…The consolidation of DFM values into a single score depends on the subject domain and can be conducted following two ranking strategies, namely parallel coordinate maps [Inselberg and Dimsdale 1990] and ranking aggregation [Fagin et al 2003;Ciaccia and Martinenghi 2017].…”

Section: The Diversity Features Model -Dfmmentioning

confidence: 99%

An empirical assessment of quality metrics for diversified similarity searching

Lopes¹,

Santos²,

Jasbick

et al. 2021

jidm

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A diversified similarity search retrieves elements that are simultaneously similar to a query object and akin to the different collections within the explored data. While several methods in information retrieval, data clustering, and similarity searching have tackled the problem of adding diversity into result sets, the experimental comparison of their performances is still an open issue mainly because the quality metrics are “borrowed” from those different research areas, bringing their biases alongside. In this manuscript, we investigate a series of such metrics and experimentally discuss their trends and limitations. We conclude diversity is better addressed by a set of measures rather than a single quality index and introduce the concept of Diversity Features Model (DFM), which combines the viewpoints of biased metrics into a multidimensional representation. Experimental evaluations indicate (i) DFM enables comparing different result diversification algorithms by considering multiple criteria, and (ii) the most suitable searching methods for a particular dataset are spotted by combining DFM with ranking aggregation and parallel coordinates maps.

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“…The skyline query is a typical query type of multi‐target optimization and preference query, which is also referred as the Pareto optimal query . Let D be a set of points and the skyline of D is a subset of D and consists of all the points that are not dominated by any other point in D .…”

Section: Introductionmentioning

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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Reconciling skyline and ranking queries

Cited by 39 publications

References 25 publications

An outline of multi objective optimization in databases with focus on flexible skyline queries

An outline of multi objective optimization in databases with focus on flexible skyline queries

An empirical assessment of quality metrics for diversified similarity searching

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

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