Skyline Queries Computation on Crowdsourced- Enabled Incomplete Database

Swidan, Marwa B.; Alwan, Ali A.; Turaev, Sherzod; Ibrahim, Hamidah; Abualkishik, Abedallah Zaid; Gulzar, Yonis

doi:10.1109/access.2020.3000664

Cited by 7 publications

(6 citation statements)

References 41 publications

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“…Most of the previous studies concentrate on designing query processing operators work in a crowd-sourced database aiming at improving the accuracy of the query result with reasonable cost and time. Cost and result accuracy are among the most critical factors that have been considered in most of the previous works (Franklin et al, 2011;Parameswaran et al, 2012b;Lofi et al, 2013;Wang et al, 2013;Swidan et al, 2018;Chai et al, 2019;Lian et al, 2020;Swidan et al, 2020). To reduce the monetary cost of extracting and generating data from the crowd, many optimizations methods have been utilized such as decreasing the number of questions that submit to the crowd or put a specific budget to process the query (Ciceri, et al, 2015).…”

Section: Discussion and Future Research Challengesmentioning

confidence: 99%

“…This is accomplished by exploiting the implicit relationship between the databases of the crowd. Among the remarkable works that address the issue of preference queries on crowd-sourcing incomplete databases are (Lofi et al, 2013;Swidan et al, 2018;Miao et al, 2019;Swidan et al, 2020). We suggest that many research opportunities should be explored addressing the challenges of incomplete and uncertain data when processing queries in a crowd-sourcing database.…”

Section: Discussion and Future Research Challengesmentioning

confidence: 99%

“…The crowdsourced platforms became an effective solution for various types of queries by exploiting knowledge, ideas, experiences, and skills of crowd workers to process information and obtain accurate answers for queries. The hybrid human/machine systems defined as the process that integrates the crowd workers with computer systems to get high-quality results (Parameswaran et al, 2011;Li et al, 2016;Swidan et al, 2018;Bhaskar et al, 2020;Lian et al, 2020;Swidan et al, 2020). The crowd-sourced system consists of three main components that work together to provide the users with the best answers for the given queries.…”

Section: Introductionmentioning

confidence: 99%

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An Overview of Query Processing on Crowdsourced Databases

Swidan¹,

Alwan²

2020

SJKFU

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Crowd-sourcing is a powerful solution to correctly answer expensive and unanswered queries in the database. This includes queries on a database with uncertain and incomplete data. The crowd-sourcing attempts to exploit human abilities to process these difficult tasks and workers helped to provide accurate results utilizing the available data in the crowd. The crowd-sourcing database systems (CSDB) combined the ability of the crowd with the relational database by using some variant of the relational database with minor changes. This paper surveys and examines the leading studies conducted on the area of query processing for both traditional and preference queries in crowd-sourcing databases. The focus is given on highlights the strengths and the weakness of each approach. A detailed discussion for the current and future trends research relevant to query processing in the area of crow-sourced databases is also demonstrated.

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Section: Discussion and Future Research Challengesmentioning

confidence: 99%

Section: Discussion and Future Research Challengesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Overview of Query Processing on Crowdsourced Databases

Swidan¹,

Alwan²

2020

SJKFU

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“…A window 7 32bit machine with 3 GB memory and i3 1.6GHz Processor has been used to run a comprehensive and intensive set of experiments. It is a known fact that skyline processing is a CPU exhaustive process [1][2][8] [12][31] [40][41] [42], thus the experiments of research work involved three performance metrics: i) domination tests between tuples, ii) processing time and iii) amount of data transfer from one data center to another. To measure these three parameters two well-known databases have been used (synthetic and real).…”

Section: Experimental Environmentmentioning

confidence: 99%

CIDS: An Efficient Algorithm for Processing Skyline Queries for Partially Complete Data in Cloud Environment

Gulzar

Alwan

2022

IEEE Access

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From a set of existing tuples, a skyline operator retrieves only a subset, superior tuples that are of a person's interest and are non-dominant. Processing of queries using the skyline operator is an expensive and exhaustive task. It gets more complicated when skyline queries are applied on partially complete data and databases are distributed over different data centers. Incompleteness in data raises many issues such as compromise on transitivity property and the threat of cyclic dominance to occur within database. To overcome such issues this paper proposes an efficient algorithm called Cloud-based Incomplete Data Skyline algorithm (CIDS) for processing skyline queries over partially complete databases in cloud environment. The algorithm retrieves superior tuples with the aim of reducing domination tests between the tuples, decreasing processing time and reducing the huge amount of data flow from one data center to another. Several experiments have been conducted over different types of datasets, and results have proven that the proposed algorithm outplays the existing algorithms in terms of processing time, domination tests as well as the amount of data flow.

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“…Throughout this paper, we assume that bigger values are preferred over smaller ones. For instance, consider the tuples pi(7,6,2,8) and pj…”

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confidence: 99%

IDSA: An Efficient Algorithm for Skyline Queries Computation on Dynamic and Incomplete Data With Changing States

et al. 2021

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Skyline queries have been widely used as an effective query tool in many contemporary database applications. The main concept of skyline queries relies on retrieving the non-dominated tuples in the database which are known skylines. In most database applications, the contents of the databases are dynamic due to the continuous changes made towards the database. Typically, the changes in the contents of the database occur through data manipulation operations (INSERT and/or UPDATE). Performing these operations on the database results in invalidating the most recent skylines before changes are made on the database. Furthermore, the presence of incomplete data in databases becomes frequent phenomena in recent database applications. Data incompleteness causes several challenges on skyline queries such as losing the transitivity property of the skyline technique and the test dominance process between tuples being cyclic. Reapplying skyline technique on the entire updated incomplete database to determine the new skylines is unwise due to the exhaustive pairwise comparisons. Thus, this paper proposes an approach, named Incomplete Dynamic Skyline Algorithm (IDSA) which attempts to determine the skylines on dynamic and incomplete databases. Two optimization techniques have been incorporated in IDSA, namely: pruning and selecting superior local skylines. The pruning process attempts to exploit the derived skylines before the INSERT/UPDATE operation made on the database to identify the new skylines. Moreover, selecting superior local skylines process assists in further eliminating the remaining non-skylines from further processing. These two optimization techniques lead to a large reduction in the number of domination tests due to avoiding recomputing of skylines over the entire updated database to derive the new skylines. Extensive experiments have been accomplished on both real and synthetic datasets, and the results demonstrate that IDSA outperforms the existing solutions in terms of the number of domination tests and the processing time of the skyline operation.INDEX TERMS Dynamic database, incomplete database, pairwise comparison, skyline queries.

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Skyline Queries Computation on Crowdsourced- Enabled Incomplete Database

Cited by 7 publications

References 41 publications

An Overview of Query Processing on Crowdsourced Databases

An Overview of Query Processing on Crowdsourced Databases

CIDS: An Efficient Algorithm for Processing Skyline Queries for Partially Complete Data in Cloud Environment

IDSA: An Efficient Algorithm for Skyline Queries Computation on Dynamic and Incomplete Data With Changing States

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