Refactorings of Design Defects using Relational Concept Analysis

Software engineers often need to identify and correct design defects, ıe} recurring design problems that hinder development and maintenance\ud by making programs harder to comprehend and--or evolve. While detection\ud of design defects is an actively researched area, their correction---mainly\ud a manual and time-consuming activity --- is yet to be extensively\ud investigated for automation. In this paper, we propose an automated\ud approach for suggesting defect-correcting refactorings using relational\ud concept analysis (RCA). The added value of RCA consists in exploiting\ud the links between formal objects which abound in a software re-engineering\ud context. We validated our approach on instances of the <span class='textit'></span>Blob\ud design defect taken from four different open-source programs

Scalable aggregation predictive analytics: a query-driven machine learning approach

We introduce a predictive modeling solution that provides high quality predictive analytics over aggregation queries in Big Data environments. Our predictive methodology is generally applicable in environments in which large-scale data owners may or may not restrict access to their data and allow only aggregation operators like COUNT to be executed over their data. In this context, our methodology is based on historical queries and their answers to accurately predict ad-hoc queries’ answers. We focus on the widely used set-cardinality, i.e., COUNT, aggregation query, as COUNT is a fundamental operator for both internal data system optimizations and for aggregation-oriented data exploration and predictive analytics. We contribute a novel, query-driven Machine Learning (ML) model whose goals are to: (i) learn the query-answer space from past issued queries, (ii) associate the query space with local linear regression &amp; associative function estimators, (iii) define query similarity, and (iv) predict the cardinality of the answer set of unseen incoming queries, referred to the Set Cardinality Prediction (SCP) problem. Our ML model incorporates incremental ML algorithms for ensuring high quality prediction results. The significance of contribution lies in that it (i) is the only query-driven solution applicable over general Big Data environments, which include restricted-access data, (ii) offers incremental learning adjusted for arriving ad-hoc queries, which is well suited for query-driven data exploration, and (iii) offers a performance (in terms of scalability, SCP accuracy, processing time, and memory requirements) that is superior to data-centric approaches. We provide a comprehensive performance evaluation of our model evaluating its sensitivity, scalability and efficiency for quality predictive analytics. In addition, we report on the development and incorporation of our ML model in Spark showing its superior performance compared to the Spark’s COUNT method

Complementarities and systems: Understanding japanese economic organization

The performance of the Japanese economy in the last forty five years, during which it has gone from post war destitution and near collapse to one of the richest and most productive in the world is unmatched in human history. The purposes of this essay are to interpret both the characteristic features of Japanese economic organization in terms of the concept of complementarity, and some recent developments in Japanese economy, and to speculate on its future.

Using formal concept analysis for the verification of process-data matrices in conceptual domain models.

One of the first steps in a software engineering process is the elaboration of the conceptual domain model. In this paper, we investigate how Formal Concept Analysis can be used to formally underpin the construction of a conceptual domain model. In particular, we demonstrate that intuitive verification rules for process-data matrices can be formally grounded in FCA theory. As a case study, we show that the well-formedness rules from MERODE are isomorphic to the clustering rules in Formal Concept Analysis, and that the relationships in the class diagram are isomorphic to the subconcept-superconcept relationship in FCA.Formal concept analysis; MERODE; Conceptual domain modeling; OOSSADM; CRUD;