Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

HyDRA Hybrid workflow Design Recommender Architecture

Workflows are a way to describe a series of computations on raw e-Science data. These data may be MRI brain scans, data from a high energy physics detector or metric data from an earth observation project. In order to derive meaningful knowledge from the data, it must be processed and analysed. Workflows have emerged as the principle mechanism for describing and enacting complex e-Science analyses on distributed infrastructures such as grids. Scientific users face a number of challenges when designing workflows. These challenges include selecting appropriate components for their tasks, spec- ifying dependencies between them and selecting appropriate parameter values. These tasks become especially challenging as workflows become increasingly large. For example, the CIVET workflow consists of up to 108 components. Building the workflow by hand and specifying all the links can become quite cumbersome for scientific users.Traditionally, recommender systems have been employed to assist users in such time-consuming and tedious tasks. One of the techniques used by recommender systems has been to predict what the user is attempting to do using a variety of techniques. These techniques include using workflow se- mantics on the one hand and historical usage patterns on the other. Semantics-based systems attempt to infer a user’s intentions based on the available semantics. Pattern-based systems attempt to extract usage patterns from previously-constructed workflows and match those patterns to the workflow un- der construction. The use of historical patterns adds dynamism to the suggestions as the system can learn and adapt with “experience”. However, in cases where there are no previous patterns to draw upon, pattern-based systems fail to perform. Semantics-based systems, on the other hand infer from static information, so they always have something to draw upon. However, that information first has to be encoded into the semantic repository for the system to draw upon it, which is a time-consuming and tedious task in it self. Moreover, semantics-based systems do not learn and adapt with experience. Both approaches have distinct, but complementary features and drawbacks. By combining the two approaches, the drawbacks of each approach can be addressed.This thesis presents HyDRA, a novel hybrid framework that combines frequent usage patterns and workflow semantics to generate suggestions. The functions performed by the framework include; a) extracting frequent functional usage patterns; b) identifying the semantics of unknown components; and c) generating accurate and meaningful suggestions. Challenges to mining frequent patterns in- clude ensuring that meaningful and useful patterns are extracted. For this purpose only patterns that occur above a minimum frequency threshold are mined. Moreover, instead of just groups of specific components, the pattern mining algorithm takes into account workflow component semantics. This allows the system to identify different types of components that perform a single composite function. One of the challenges in maintaining a semantic repository is to keep the repository up-to-date. This involves identifying new items and inferring their semantics. In this regard, a minor contribution of this research is a semantic inference engine that is responsible for function b). This engine also uses pre-defined workflow component semantics to infer new semantic properties and generate more accurate suggestions. The overall suggestion generation algorithm is also presented.HyDRA has been evaluated using workflows from the Laboratory of Neuro Imaging (LONI) repos- itory. These workflows have been chosen for their structural and functional characteristics that help� to evaluate the framework in different scenarios. The system is also compared with another existing pattern-based system to show a clear improvement in the accuracy of the suggestions generated

Bayesian network analysis of software logs for data-driven software maintenance

Software organisations aim to develop and maintain high-quality software systems. Due to large amounts of behaviour data available, software organisations can conduct data-driven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data-driven software maintenance. Finally, the authors consider that BNs within quality-aware and data-driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared.Junta de Andalucía, Grant/Award Number: P20‐00091; AEI, Grant/Award Number: PID2019‐106758GB‐32/AEI/10.13039/501100011033; Spanish project, Grant/Award Number: PDC2021‐121195‐I00; Spanish Program, Grant/Award Number: BEAGAL18/00064Peer ReviewedPostprint (published version

TLAD 2010 Proceedings:8th international workshop on teaching, learning and assesment of databases (TLAD)

This is the eighth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2010), which once again is held as a workshop of BNCOD 2010 - the 27th International Information Systems Conference. TLAD 2010 is held on the 28th June at the beautiful Dudhope Castle at the Abertay University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.This year, the workshop includes an invited talk given by Richard Cooper (of the University of Glasgow) who will present a discussion and some results from the Database Disciplinary Commons which was held in the UK over the academic year. Due to the healthy number of high quality submissions this year, the workshop will also present seven peer reviewed papers, and six refereed poster papers. Of the seven presented papers, three will be presented as full papers and four as short papers. These papers and posters cover a number of themes, including: approaches to teaching databases, e.g. group centered and problem based learning; use of novel case studies, e.g. forensics and XML data; techniques and approaches for improving teaching and student learning processes; assessment techniques, e.g. peer review; methods for improving students abilities to develop database queries and develop E-R diagrams; and e-learning platforms for supporting teaching and learning