Fuzziness in database management systems: Half a century of developments and future prospects

Kacprzyk, Janusz; Zadrożny, Sławomir; Tré, Guy De

doi:10.1016/j.fss.2015.06.011

Cited by 54 publications

(18 citation statements)

References 103 publications

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“…One trend in this direction is providing natural language interfaces to RDBMS. This will allow domain experts who are in general not database experts to access and retrieve data without feeling the need to consult a SQL expert [32].…”

Section: Problem Definition and Motivationmentioning

confidence: 99%

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Integrating flexibility and fuzziness into a question driven query model

Sarhan

Rokne

Alhajj

2018

Information Sciences

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Data plays an important role in our daily life. Thus, data collection, storage, maintenance and processing continue to attract considerable attention. Data may exist in various formats, ranging from unstructured to structured as the two extremes. Traditionally, researchers and practitioners cooperated and developed various data models which form the main foundation for existing database management systems. The relational data model is still dominating despite the rapid development in the techniques used for data collection, storage and processing. Further, a relational database management system supports a structured query language (SQL) for data processing, and it is not possible to access and retrieve data from a relational database without knowing how to use SQL. However, the wide usage of relational databases motivated researchers to develop more user friendly interfaces which would allow a larger population of users to access relational databases. Such interfaces range from visual to natural language based. This thesis contributes a question driven query model which falls under the natural language based category. The target is to make databases reachable by a larger population, especially after the Internet increased database availability. The proposed model supports fuzziness where every user is given the freedom to define his/her own understanding of fuzzy terms. The developed system absorbs the fuzzy understanding of each user to utilize it while deciding on the result to be communicated back as answer to the raised question. Data mining techniques are employed to guide users in defining their fuzzy understanding. The developed model is intended to help users to retrieve the data they want from a relational database without expecting them to know SQL. In the current version only questions written in English are allowed. The system handles different types of questions, such as (1) simple questions, (2) complex questions with inner joins and where conditions, (3) questions that involves the usage of aggregate functions (e.g., min, max, etc.), and (4) questions with fuzzy i terms. The reported test results demonstrate the effectiveness of the developed system in handling various types of questions raised by a heterogeneous set of users ranging from professionals to naive.

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Section: Problem Definition and Motivationmentioning

confidence: 99%

“…Researchers have been working extensively on developing algorithms that can handle this task. Moreover, many approaches have been proposed to include fuzziness in SQL, e.g., [32,4,8,39,6,9]. The work described in this thesis focuses on translating users' requests into valid SQL queries, regardless whether the requests are fuzzy or not.…”

Section: Related Workmentioning

confidence: 99%

Integrating flexibility and fuzziness into a question driven query model

Sarhan

Rokne

Alhajj

2018

Information Sciences

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“…Non-deterministic access control models could also be considered for implementation of sequences of actions, especially when branches are considered and users can follow any of them. Several of these models exist, of which we emphasize: probabilistic models to determine risk [15][16] [17], cognitive-based systems [18] and fuzzy theory-based models [19] [20]. Ultimately, the SeqBAC model presented here is meant to be deterministic, which would allow security experts to conduct auditing and to keep a level of assurance that no unexpected access decision is made.…”

Section: Related Workmentioning

confidence: 99%

SeqBAC: A Sequence-Based Access Control Model (S)

Regateiro

Pereira

Aguiar

2018

International Conferences on Software Engineering and Knowledge Engineering

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Access control, when used in the context of database applications, is aimed to supervise the requests made by legitimate users to access sensitive data. These requests represent actions that a user can perform on a database and they typically read or write data. While this supervision can be formalized at a higher level, e.g. using an access control model such as RBAC, in the end, the data access is done through each authorized action. Therefore, the current access control models enforce their policies on an action by action basis, being unable to support relations of order between them. In many database applications, access to data is not done randomly, but by following very specific sequences of actions which are not supervised. This paper argues that a better security policy can be achieved by supervising these sequences. Thus, previous research is leveraged to propose a formalized model, capable of enforcing access control over the sequences of actions that can complement existing access control models.

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“…Fuzzy querying is not a new concept (see [26,27] for early works, and [5,28] for more recent ones), and it has been mainly applied as an extension of traditional querying in RDBS, enabling users to query them in natural language (inherently imprecise and vague). Our work can be considered as related with fuzzy querying because we will consistently ask for the interesting nodes within a graph.…”

Section: Fuzzy Data Explorationmentioning

confidence: 99%

“…These are appropriate formalisms to handle imprecise knowledge, and particularly, imprecise queries over the data [5]. That kind of queries is even more relevant when performing exploratory data analysis over big datasets, and has its maximum exponent in the query "which are the most interesting pieces of data?…”

Section: Introductionmentioning

confidence: 99%

Improving data exploration in graphs with fuzzy logic and large-scale visualisation

Molina-Solana

Birch

Guo

2017

Applied Soft Computing

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This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.

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Fuzziness in database management systems: Half a century of developments and future prospects

Cited by 54 publications

References 103 publications

Integrating flexibility and fuzziness into a question driven query model

Integrating flexibility and fuzziness into a question driven query model

SeqBAC: A Sequence-Based Access Control Model (S)

Improving data exploration in graphs with fuzzy logic and large-scale visualisation

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