Visual analytics in FCA-based clustering

Visual analytics is a subdomain of data analysis which combines both human and machine analytical abilities and is applied mostly in decision-making and data mining tasks. Triclustering, based on Formal Concept Analysis (FCA), was developed to detect groups of objects with similar properties under similar conditions. It is used in Social Network Analysis (SNA) and is a basis for certain types of recommender systems. The problem of triclustering algorithms is that they do not always produce meaningful clusters. This article describes a specific triclustering algorithm and a prototype of a visual analytics platform for working with obtained clusters. This tool is designed as a testing frameworkis and is intended to help an analyst to grasp the results of triclustering and recommender algorithms, and to make decisions on meaningfulness of certain triclusters and recommendations.Comment: 11 pages, 3 figures, 2 algorithms, 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST'2014). in Supplementary Proceedings of the 3rd International Conference on Analysis of Images, Social Networks and Texts (AIST 2014), Vol. 1197, CEUR-WS.org, 201

Towards automated data integration in software analytics

Software organizations want to be able to base their decisions on the latest set of available data and the real-time analytics derived from them. In order to support "real-time enterprise" for software organizations and provide information transparency for diverse stakeholders, we integrate heterogeneous data sources about software analytics, such as static code analysis, testing results, issue tracking systems, network monitoring systems, etc. To deal with the heterogeneity of the underlying data sources, we follow an ontology-based data integration approach in this paper and define an ontology that captures the semantics of relevant data for software analytics. Furthermore, we focus on the integration of such data sources by proposing two approaches: a static and a dynamic one. We first discuss the current static approach with a predefined set of analytic views representing software quality factors and further envision how this process could be automated in order to dynamically build custom user analysis using a semi-automatic platform for managing the lifecycle of analytics infrastructures.Peer ReviewedPostprint (author's final draft

Project Testbed: Argument Mapping and Deliberation Analytics

One key goal of the Catalyst project was to design metrics that could capture and represent aspects of the conversation’s structural quality, to assist harvesters and moderators. Many such metrics, alerts and visualizations were developed in the course of the project, but initial user testing has shown that users find it difficult to interpret abstract signals. Following that, we have both introduced new analytics that we felt could be more directly useful, and improved the representation of existing ones. We evaluated their usefulness in a smaller conversation and in experimental settings

Towards Automated Data Integration in Software Analytics

Software organizations want to be able to base their decisions on the latest set of available data and the real-time analytics derived from them. In order to support "real-time enterprise" for software organizations and provide information transparency for diverse stakeholders, we integrate heterogeneous data sources about software analytics, such as static code analysis, testing results, issue tracking systems, network monitoring systems, etc. To deal with the heterogeneity of the underlying data sources, we follow an ontology-based data integration approach in this paper and define an ontology that captures the semantics of relevant data for software analytics. Furthermore, we focus on the integration of such data sources by proposing two approaches: a static and a dynamic one. We first discuss the current static approach with a predefined set of analytic views representing software quality factors and further envision how this process could be automated in order to dynamically build custom user analysis using a semi-automatic platform for managing the lifecycle of analytics infrastructures.Comment: This is an author's accepted manuscript of a paper to be published by ACM in the 12th International Workshop on Real-Time Business Intelligence and Analytics (BIRTE@VLDB) 2018. The final authenticated version will be available through https://doi.org/10.1145/3242153.324215

SIT automation tool: failure use case automation and diagnosis

Study of systems to manage the performance and quality of service of wireless data networks. Work with optimization techniques and project management to solve complex networks issues.The scope of this thesis is the SIT (System Integration Testing) process which is the testing procedure executed in customer test environment before the software goes on production environment. The main objective for this thesis is no other than improving the current process step by step taking into account the automation, efficiency, missing checks and much more. This project is a kind of Industrial process to create a powerful testing tool which can allow the company to deliver quality adaptor products efficiently, do better in less time helping to reduce costs, as Adaptors are the most demanded product of MYCOM OSI portfolio. Take into account that business is not only generated when an Adaptor is delivered for first time but also when Vendors provide with new releases and new functionalities and operators needs to order an upgrade of the Adaptor to be able to monitor the new functionalities deployed on their network.El campo de aplicación en el que está centrado esta tesis es el SIT (System Integration Testing), proceso de testeo ejecutado en un servidor de testeo del cliente antes de desplegar el software el medio de producción. El objetivo principal de esta tesis no es otro que mejorar el proceso actual paso a paso teniendo en cuenta la automatización, eficiencia, la falta de verificaciones, entre otros. Este proyecto es una especie de proceso industrial para crear una aplicación potente de testeo que pueda permitir a la compañía entregar adaptadores de calidad con eficiencia, que hagan más en menos tiempo ayudando así a reducir costes. Los adaptadores son el producto más demandado del porfolio de MYCOM OSI. Hay que tener en cuenta que el negocio no se genera solamente cuando se entrega por primera vez el adaptador al cliente, sino que cuando los proveedores lanzan nuevas versiones con nuevas funcionalidades y los operadores necesitan encargar una mejora del adaptador para poder monitorizar las nuevas funcionalidades desplegadas en su red.El camp d'aplicació en que es basa aquesta tesi és el SIT (System Integration Testing), procés de testeig executat en un servidor de testeig del client abans de desplegar el software al mitjà de producció. L'objectiu principal d'aquesta tesi no és un altre que millorar el procés actual pas a pas tenint en compte l'automatització, l'eficiència, la falta de verificacions, d'entre altres. Aquest projecte és una mena de procés industrial per crear una aplicació potent de testeig que pugui permetre a la companyia lliurar adaptadors de qualitat amb eficiència, que facin més en menys temps ajudant així a reduir costos. Els adaptadors són el producte més demandat del porfolio de MYCOM OSI. Cal tenir en compte que el negoci no només es genera quan es lliura per primera vegada l'adaptador al client, sinó que quan els proveïdors llancen noves versions amb noves funcionalitats i els operadors necessiten encarregar una millora de l'adaptador per poder monitoritzar les noves funcionalitats desplegades a la seva xarxa

Easy over Hard: A Case Study on Deep Learning

While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost. This is particularly important for deep learning since these learners need hours (to weeks) to train the model. Such long training time limits the ability of (a)~a researcher to test the stability of their conclusion via repeated runs with different random seeds; and (b)~other researchers to repeat, improve, or even refute that original work. For example, recently, deep learning was used to find which questions in the Stack Overflow programmer discussion forum can be linked together. That deep learning system took 14 hours to execute. We show here that applying a very simple optimizer called DE to fine tune SVM, it can achieve similar (and sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84 times faster hours than deep learning method. We offer these results as a cautionary tale to the software analytics community and suggest that not every new innovation should be applied without critical analysis. If researchers deploy some new and expensive process, that work should be baselined against some simpler and faster alternatives.Comment: 12 pages, 6 figures, accepted at FSE201