Big Data Semantics

Ceravolo, Paolo; Azzini, Antonia; Angelini, Marco; Catarci, Tiziana; Cudré-Mauroux, Philippe; Damiani, Ernesto; Mazak, Alexandra; Keulen, Maurice van; Jarrar, Mustafa; Santucci, Giuseppe; Sattler, Kai-Uwe; Scannapieco, Monica; Wimmer, Manuel; Wrembel, Robert; Zaraket, Fadi A.

doi:10.1007/s13740-018-0086-2

Cited by 53 publications

(37 citation statements)

References 141 publications

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“…Table VII lists the tools cited more than once. Of all the tools, Apache Hadoop was the most mentioned, having 37 citations ( [1], [6], [8], [9], [12], [18], [23]- [25], [27], [28], [30], [31], [40], [42]- [44], [47], [48], [50], [52], [55], [61], [65], [67]- [71], [73], [76], [80], [82]- [86], [88]). Hadoop is a platform that allows distributed processing of large data sets in computer clusters.…”

Section: Resultsmentioning

confidence: 99%

“…Initial Accepted Scopus 108 53 papers: [1]- [3], [5], [9], [10], [13]- [19], [23]- [29], [31]- [33], [37], [40], [45], [49], [50], [57], [60]- [66], [68], [70], [71], [73], [76]- [78], [81]- [84], [88], [90], [91], [93]- [95] Springer 222 20 papers: [4], [6], [12], [21], [30], [36], [38], [39], [41]- [43], [47], [51], [53], [69], [74], [79], [85], [86], [92] Google Scholar 197 6 papers:...…”

Section: Sourcementioning

confidence: 99%

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A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Couto¹,

Borges²,

Ruiz³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

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In the past few years, data lakes emerged as a trending topic in big data technologies. Although literature presents different points of view related to its functionalities, it serves mainly to store a variety of data in a big data context. In this paper, we aim to identify and analyze data lake definitions and possible architectures. Our methodology was composed of a systematic literature mapping based on PRISMA, software engineering best practices to perform reviews, and Kappa method to assess results' quality. We performed the search in eight different electronic databases to achieve a wide variety of publishers in Computer Science. We first identified 662 papers matching our search criteria; after filtering, we selected 87 papers for review. We found that the term data lakes was first defined by James Dixon in 2010. We also found that the term is often related to raw data repositories. From the identified definitions, we propose a new one as a means to better state what data lakes refer to and improve how the community use them. Moreover, we foind that Hadoop and its ecosystem compose the most used toolset to create data lakes, revealing that this is the mainstream in architectures for data lakes as of today's available technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Sourcementioning

confidence: 99%

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Couto¹,

Borges²,

Ruiz³

et al. 2019

International Conferences on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…The demand to analyze data stored in such systems is increasing and one of the analysis options is to use data warehousing. Open Big Data challenges and research directions have been outlined in several recent articles (Abaker et al, 2014;Ceravolo et al, 2018;Cuzzocrea et al, 2013;Kaisler et al, 2013;Holubov et al, 2019). The authors of the paper (Kaisler et al, 2013) mention dynamic design challenges for big data applications, which include data expansion that occurs when data becomes more detailed.…”

Section: Introductionmentioning

confidence: 99%

“…Among others, the authors also mention the problem of designing OLAP cubes according to user requirements. In the paper (Ceravolo et al, 2018) the authors state that detecting data and structural changes and handling them in a Big Data system is a complex task that requires further research. Another recent vision paper (Holubov et al, 2019) analyses research challenges in multi-model environments and suggests that efficient management of schema evolution and propogation of schema changes to affected parts of the system is a complex task and one of the topical issues.…”

Section: Introductionmentioning

confidence: 99%

Handling Evolution in Big Data Architectures

Solodovnikova¹,

Niedrite²

2020

BJMC

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One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources. Since data warehouses have been used to achieve the same goal, they could be leveraged also to provide analysis of Big Data. The problem of adapting data warehouse data and schemata to changes in user requirements and data sources has been studied by many researchers worldwide. However, innovative methods must be developed also to support evolution in Big Data systems. In this paper, we analyze architectures designed for Big Data processing and analysis described in the literature with the purpose to identify the most appropriate solution for the evolution problem. We concentrate on four architecture types: data lakes, virtual integration, polystores, and λ-architecture, and, in addition to them, we consider solutions that apply data warehouse/OLAP methods to Big Data processing and analysis. Finally, we describe our proposal of an architecture that allows to perform different kinds of analytical tasks on Big Data retrieved from multiple heterogeneous data sources with different latency and is capable of processing changes in data sources as well as evolving analysis requirements.

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“…The problem got even more difficult with the advent of multi-enterprise business coalitions: besides having a huge size, today's process streams are generated, collected and analyzed asynchronously, at different locations and under the control of different organizations. In such scenarios, writing the filter corresponding to a given AC policy manually is awkward and error-prone, and solutions targeting Big Data source are needed [7,8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Access Control to Semantics-Aware Streamed Process Logs

et al. 2019

Self Cite

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Business process logs are composed of event records generated, collected and analyzed at different locations, asynchronously and under the responsibility of different authorities. Their analysis is often delegated to auditors who have a mandate for monitoring processes and computing metrics but do not always have the rights to access the individual events used to compute them. A major challenge of this scenario is reconciling the requirements of privacy and access control with the need to continuously monitor and assess the business process. In this paper, we present a model, a language and a software toolkit for controlling access to process data where logs are made available as streams of RDF triples referring to some company-specific business ontology. Our approach is based on the novel idea of dynamic enforcement: we incrementally build dynamic filters for each process instance, based on the applicable access control policy and on the current prefix of the event stream. The implementation and performance validation of our solution is also presented.

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Big Data Semantics

Cited by 53 publications

References 141 publications

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

A Mapping Study about Data Lakes: An Improved Definition and Possible Architectures

Handling Evolution in Big Data Architectures

Dynamic Access Control to Semantics-Aware Streamed Process Logs

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