Data-centric API configuration: inconsistency detection and diagnosis

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Title: Data-centric API configuration: inconsistency detection and diagnosis
Author: Bui, Minh
Contributor: University of Helsinki, Faculty of Science
Publisher: Helsingin yliopisto
Date: 2021
Language: eng
Thesis level: master's thesis
Degree program: Tietojenkäsittelytieteen maisteriohjelma
Master's Programme in Computer Science
Magisterprogrammet i datavetenskap
Specialisation: Ohjelmistojärjestelmät
Software systems
Abstract: Background. In API requests to a confidential data system, there always are sets of rules that the users must follow to retrieve desired data within their granted permission. These rules are made to assure the security of the system and limit all possible violations. Objective. The thesis is about detecting the violations of these rules in such systems. For any violation found, the request is considered as containing inconsistency and it must be fixed before retrieving any data. This thesis also looks for all diagnoses of inconsistencies requests. These diagnoses promote reconstructing the requests to remove any inconsistency. Method. In this thesis, we choose the design science research methodology to work on solutions. In this methodology, the current problem in distributing data from a smart building plays as the main motivation. Then, system design and development are implemented to prove the found solutions of practicality, while a testing system is built to confirm its validity. Results. The inconsistencies detection is considered as a diagnostic problem, and many algorithms have been found to resolve the diagnostic problem for decades. The algorithms are developed based on DAG algorithms and preserved to apply on different purposes. This thesis is based on these algorithms and constraint programming techniques to resolve the facing issues of the given confidential data system. Conclusions. A combination of constraint programming techniques and DAG algorithms for diagnostic problems can be used to resolve inconsistencies detection in API requests. Despite the need on performance improvement in application of these algorithms, the combination works effectively, and can resolve the research problem.

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