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DAGGTAX: A Taxonomy of Data Aggregation Processes
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Formal Modelling and Analysis of Embedded Systems)
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Dependable Software Engineering)ORCID-id: 0000-0002-6952-1053
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Model-Based Engineering of Embedded Systems)ORCID-id: 0000-0003-2898-9570
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system. (Formal Modelling and Analysis of Embedded Systems)ORCID-id: 0000-0003-2870-2680
2017 (Engelska)Rapport (Övrigt vetenskapligt)
Abstract [en]

Data aggregation processes are essential constituents in many data management applications. Due to their complexity, designing data aggregation processes often demands considerable efforts. A study on the features of data aggregation processes will provide a comprehensive view for the designers and ease the design process. Existing works either propose application-specific aggregation solutions, or focus on particular aspects of aggregation processes such as aggregate functions, hence they do not offer a high-level, generic description. In this paper, we propose a taxonomy of data aggregation processes called DAGGTAX, which builds on the results of an extensive survey within various application domains. Our work focuses on the features of aggregation processes and their implications, especially on the temporal data consistency and the process timeliness. We present our taxonomy as a feature diagram, which is a visual notation with formal semantics. The taxonomy can then serve as the foundation of a design tool that enables designers to build an aggregation process by selecting and composing desired features. Based on the implications of the features, we formulate three design rules that eliminate infeasible feature combinations. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. 

Ort, förlag, år, upplaga, sidor
Västerås, 2017.
Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:mdh:diva-35366ISRN: MDH-MRTC-319/2017-1-SEOAI: oai:DiVA.org:mdh-35366DiVA, id: diva2:1097298
Projekt
DAGGERS
Forskningsfinansiär
KK-stiftelsenTillgänglig från: 2017-05-22 Skapad: 2017-05-22 Senast uppdaterad: 2017-05-29Bibliografiskt granskad
Ingår i avhandling
1. Systematic Design of Data Management for Real-Time Data-Intensive Applications
Öppna denna publikation i ny flik eller fönster >>Systematic Design of Data Management for Real-Time Data-Intensive Applications
2017 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Modern real-time data-intensive systems generate large amounts of data that are processed using complex data-related computations such as data aggregation. In order to maintain the consistency of data, such computations must be both logically correct (producing correct and consistent results) and temporally correct (completing before specified deadlines). One solution to ensure logical and temporal correctness is to model these computations as transactions and manage them using a Real-Time Database Management System (RTDBMS). Ideally, depending on the particular system, the transactions are customized with the desired logical and temporal correctness properties, which are achieved by the customized RTDBMS with appropriate run-time mechanisms. However, developing such a data management solution with provided guarantees is not easy, partly due to inadequate support for systematic analysis during the design. Firstly, designers do not have means to identify the characteristics of the computations, especially data aggregation, and to reason about their implications. Design flaws might not be discovered, and thus they may be propagated to the implementation. Secondly, trade-off analysis of conflicting properties, such as conflicts between transaction isolation and temporal correctness, is mainly performed ad-hoc, which increases the risk of unpredictable behavior.

In this thesis, we propose a systematic approach to develop transaction-based data management with data aggregation support for real-time systems. Our approach includes the following contributions: (i) a taxonomy of data aggregation, (ii) a process for customizing transaction models and RTDBMS, and (iii) a pattern-based method of modeling transactions in the timed automata framework, which we show how to verify with respect to transaction isolation and temporal correctness. Our proposed taxonomy of data aggregation processes helps in identifying their common and variable characteristics, based on which their implications can be reasoned about. Our proposed process allows designers to derive transaction models with desired properties for the data-related computations from system requirements, and decide the appropriate run-time mechanisms for the customized RTDBMS to achieve the desired properties. To perform systematic trade-off analysis between transaction isolation and temporal correctness specifically, we propose a method to create formal models of transactions with concurrency control, based on which the isolation and temporal correctness properties can be verified by model checking, using the UPPAAL tool. By applying the proposed approach to the development of an industrial demonstrator, we validate the applicability of our approach.

Ort, förlag, år, upplaga, sidor
Västerås: Mälardalen University, 2017
Serie
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 258
Nationell ämneskategori
Datorsystem
Identifikatorer
urn:nbn:se:mdh:diva-35369 (URN)978-91-7485-334-6 (ISBN)
Presentation
2017-06-12, Kappa, Mälardalens högskola, Västerås, 13:30 (Engelska)
Opponent
Handledare
Projekt
DAGGERS
Forskningsfinansiär
KK-stiftelsen
Tillgänglig från: 2017-05-23 Skapad: 2017-05-22 Senast uppdaterad: 2017-07-10Bibliografiskt granskad

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http://www.es.mdh.se/publications/4628-

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Cai, SiminGallina, BarbaraNyström, DagSeceleanu, Cristina

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