Trustworthy answers for top-k queries on uncertain Big Data in decision making

Nguyen, Ha; Cao, Tru H.

doi:10.1016/j.ins.2014.08.065

Cited by 29 publications

(8 citation statements)

References 9 publications

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“…As a consequence, strategic managerial decisions may now be supported by accurate information leading to increase in performance of organization by reducing costs or increasing revenues. Nguyen and Cao (2015) have studied the ways in which BDA adoption may lead to stronger collaborations between partners belonging to the same production chain. The architecture of BDA systems may incentivise the sharing of data concerning production process efficiency between partners.…”

Section: Blue Cluster: Big Data Dynamic Capabilities Decision Making and Performancementioning

confidence: 99%

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Big data and dynamic capabilities: a bibliometric analysis and systematic literature review

et al. 2019

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Purpose Recently, several manuscripts about the effects of big data on organizations used dynamic capabilities as their main theoretical approach. However, these manuscripts still lack systematization. Consequently, the purpose of this paper is to systematize the literature on big data and dynamic capabilities. Design/methodology/approach A bibliometric analysis was performed on 170 manuscripts extracted from the Clarivate Analytics Web of Science Core Collection database. The bibliometric analysis was integrated with a literature review. Findings The bibliometric analysis revealed four clusters of papers on big data and dynamic capabilities: big data and supply chain management, knowledge management, decision making, business process management and big data analytics. The systematic literature review helped to clarify each clusters’ content. Originality/value To the authors’ best knowledge, minimal attention has been paid to systematizing the literature on big data and dynamic capabilities.

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Section: Blue Cluster: Big Data Dynamic Capabilities Decision Making and Performancementioning

confidence: 99%

“…Nguyen and Cao (2015) have studied the ways in which BDA adoption may lead to stronger collaborations between partners belonging to the same production chain. The architecture of BDA systems may incentivise the sharing of data concerning production process efficiency between partners.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Big data and dynamic capabilities: a bibliometric analysis and systematic literature review

et al. 2019

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“…In [51], the authors discuss the Dominating Top-k Aggregate Query (DA-Topk) algorithm that provides trustworthy and reliable informative knowledge from uncertain large scale data. A number of pruning rules are adopted to reduce the search space and terminate the ranking process the earliest possible.…”

Section: Ordered Sets Queries Managementmentioning

confidence: 99%

An intelligent, uncertainty driven aggregation scheme for streams of ordered sets

Kolomvatsos

2016

Appl Intell

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Data streams management has attracted the attention of many researchers during the recent years. The reason is that numerous devices generate huge amounts of data demanding an efficient processing scheme for delivering high quality applications. Data are reported through streams and stored into a number of partitions. Separation techniques facilitate the parallel management of data while intelligent methods are necessary to manage these multiple instances of data. Progressive analytics over huge amounts of data could be adopted to deliver partial responses and, possibly, to save time in the execution of applications. An interesting research domain is the efficient management of queries over multiple partitions. Usually, such queries demand responses in the form of ordered sets of objects (e.g., top-k queries). These ordered sets include objects in a ranked order and require novel mechanisms for deriving responses based on partial results. In this paper, we study a setting of multiple data partitions and propose an intelligent, uncertainty driven decision making mechanism that aims to respond to streams of queries. Our mechanism delivers an ordered set of objects over a number of partial ordered subsets retrieved by each partition of data. We envision that a number of query processors are placed in front of each partition and report progressive analytics to a Query Controller (QC). The QC receives queries, assigns the task to the underlying processors and decides the right time to deliver the final ordered set to the application. We propose an aggregation model for deriving the final ordered set of objects and a Fuzzy Logic (FL) inference process. We present a Type-2 FL system that decides when the QC should stop aggregating partial subsets and return the final response to the application. We report on the performance of the proposed mechanism through the execution of a large set of experiments. Our results deal with the throughput of the QC, the quality of the final ordered set of objects and the time required for delivering the final response.

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“…The marriage of Top-and uncertain data starts a novel research issue: Top-queries in uncertain database. There are several semantics of Top-queries in uncertain database: UTop- [1], U-Ranks [1], PT- [12], PKTop- [13], Expected Ranks [14,15], and other recent research works in [16][17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

MSSUTop-k: Determining the Minimum Scan Scope for UTop-kQuery over Uncertain Data

Zhao

Yao

et al. 2015

International Journal of Distributed Sensor Networks

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The semantics of UTop-kquery is based on the possible world model, and the greatest challenge in processing UTop-kqueries is the explosion of possible world space. In this direction, several optimized algorithms have been developed. However, uncertain databases are different in data distributions under different scoring functions, which has significant influence on the performance of the existing optimizing algorithms. In this paper, we propose two novel algorithms, MSSUTop-kand quick MSSUTop-k, for determining the minimum scan scope for UTop-kquery processing. This work is important because before UTop-kquery processing is started, users hope to know in advance how many and which tuples will be involved in UTop-kquery processing. Then, they can make a balance between result precision and processing cost. So, it should be the prerequisite for answering UTop-kqueries. MSSUTop-kcan achieve accurate results but is relatively more costly in time complexity. Oppositely, quick MSSUTop-kcan only achieve approximate results but performs better in time cost. We conduct comprehensive experiments to evaluate the performance of our proposed algorithms and analyze the relationship between the data distribution and the minimum scan scope of UTop-kqueries.

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Trustworthy answers for top-k queries on uncertain Big Data in decision making

Cited by 29 publications

References 9 publications

Big data and dynamic capabilities: a bibliometric analysis and systematic literature review

Big data and dynamic capabilities: a bibliometric analysis and systematic literature review

An intelligent, uncertainty driven aggregation scheme for streams of ordered sets

MSSUTop-k: Determining the Minimum Scan Scope for UTop-kQuery over Uncertain Data

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