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Gagliardelli Luca - One of the best experts on this subject based on the ideXlab platform.
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Tecniche per l’Integrazione di Sorgenti Big Data in Ambienti di Calcolo Distribuito
Università degli studi di Modena e Reggio Emilia, 2020Co-Authors: Gagliardelli LucaAbstract:Sorgenti che forniscono grandi quantitativi di dati semi-strutturati sono disponibili sul Web in forma di tabelle, contenuti annotati (e.s. RDF) e Linked Open Data. Questi dati se debitamente manipolati e integrati tra loro o con dati proprietari, possono costituire una preziosa fonte di informazione per aziende, ricercatori e agenzie governative. Il problema principale in fase di integrazione è dato dal fatto che queste sorgenti dati sono tipicamente eterogenee e non presentano chiavi su cui poter eseguire operazioni di join per unire facilmente i record. Trovare un modo per effettuare il join senza avere le chiavi è un processo fondamentale e critico dell’integrazione dei dati. Inoltre, per molte applicazioni, il tempo di esecuzione è una componente fondamentale (e.s. nel contesto della sicurezza nazionale) e il calcolo distribuito può essere utilizzato per ridurlo sensibilmente. In questa dissertazione presento delle tecniche distribuite per l’integrazione dati che consentono di scalare su grandi volumi di dati (Big Data), in particolare: SparkER e GraphJoin. SparkER è un tool per Entity Resolution che mira ad utilizzare il calcolo distribuito per identificare record che si riferiscono alla stessa entità del mondo reale, consentendo così l’integrazione di questi record. Questo tool introduce un nuovo algoritmo per parallelizzare le tecniche di indicizzazione che sono attualmente lo stato dell’arte. SparkER è un prototipo Software funzionante che ho sviluppato e utilizzato per eseguire degli esperimenti su dati reali; i risultati ottenuti mostrano che le tecniche di parallelizzazione che ho sviluppato sono più efficienti in termini di tempo di esecuzione e utilizzo di memoria rispetto a quelle già esistenti in letteratura. GraphJoin è una nuova tecnica che consente di trovare record simili applicando delle regole di join su uno o più attributi. Questa tecnica combina tecniche di join similarity pensate per lavorare su una singola regola, ottimizzandone l’esecuzione con più regole, combinando diverse misure di similarità basate sia su token che su caratteri (e.s. Jaccard Similarity e Edit Distance). Per il GraphJoin ho sviluppato un prototipo Software funzionante e l’ho utilizzato per eseguire esperimenti che dimostrano che la tecnica proposta è efficace ed è più efficiente di quelle già esistenti in termini di tempo di esecuzione.Data sources that provide a huge amount of semi-structured data are available on Web as tables, annotated contents (e.g. RDF) and Linked Open Data. These sources can constitute a valuable source of information for companies, researchers and government agencies, if properly manipulated and integrated with each other or with proprietary data. One of the main problems is that typically these sources are heterogeneous and do not come with keys to perform join operations, and effortlessly linking their records. Thus, finding a way to join data sources without keys is a fundamental and critical process of data integration. Moreover, for many applications, the execution time is a critical component (e.g., in finance of national security context) and distributed computing can be employed to significantly it. In this dissertation, I present distributed data integration techniques that allow to scale to large volumes of data (i.e., Big Data), in particular: SparkER and GraphJoin. SparkER is an Entity Resolution tool that aims to exploit the distributed computing to identify records in data sources that refer to the same real-world entity—thus enabling the integration of the records. This tool introduces a novel algorithm to parallelize the indexing techniques that are currently state-of-the-art. SparkER is a Working Software prototype that I developed and employed to perform experiments over real data sets; the results show that the parallelization techniques that I have developed are more efficient in terms of execution time and memory usage than those in literature. GraphJoin is a novel technique that allows to find similar records by applying joining rules on one or more attributes. This technique combines similarity join techniques designed to work on a single rule, optimizing their execution with multiple joining rules, combining different similarity measures both token- and character- based (e.g., Jaccard Similarity and Edit Distance). For GraphJoin I developed a Working Software prototype and I employed it to experimentally demonstrate that the proposed technique is effective and outperforms the existing ones in terms of execution time
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Tecniche per l’Integrazione di Sorgenti Big Data in Ambienti di Calcolo Distribuito
Università degli studi di Modena e Reggio Emilia, 2020Co-Authors: Gagliardelli LucaAbstract:Sorgenti che forniscono grandi quantitativi di dati semi-strutturati sono disponibili sul Web in forma di tabelle, contenuti annotati (e.s. RDF) e Linked Open Data. Questi dati se debitamente manipolati e integrati tra loro o con dati proprietari, possono costituire una preziosa fonte di informazione per aziende, ricercatori e agenzie governative. Il problema principale in fase di integrazione \ue8 dato dal fatto che queste sorgenti dati sono tipicamente eterogenee e non presentano chiavi su cui poter eseguire operazioni di join per unire facilmente i record. Trovare un modo per effettuare il join senza avere le chiavi \ue8 un processo fondamentale e critico dell\u2019integrazione dei dati. Inoltre, per molte applicazioni, il tempo di esecuzione \ue8 una componente fondamentale (e.s. nel contesto della sicurezza nazionale) e il calcolo distribuito pu\uf2 essere utilizzato per ridurlo sensibilmente. In questa dissertazione presento delle tecniche distribuite per l\u2019integrazione dati che consentono di scalare su grandi volumi di dati (Big Data), in particolare: SparkER e GraphJoin. SparkER \ue8 un tool per Entity Resolution che mira ad utilizzare il calcolo distribuito per identificare record che si riferiscono alla stessa entit\ue0 del mondo reale, consentendo cos\uec l\u2019integrazione di questi record. Questo tool introduce un nuovo algoritmo per parallelizzare le tecniche di indicizzazione che sono attualmente lo stato dell\u2019arte. SparkER \ue8 un prototipo Software funzionante che ho sviluppato e utilizzato per eseguire degli esperimenti su dati reali; i risultati ottenuti mostrano che le tecniche di parallelizzazione che ho sviluppato sono pi\uf9 efficienti in termini di tempo di esecuzione e utilizzo di memoria rispetto a quelle gi\ue0 esistenti in letteratura. GraphJoin \ue8 una nuova tecnica che consente di trovare record simili applicando delle regole di join su uno o pi\uf9 attributi. Questa tecnica combina tecniche di join similarity pensate per lavorare su una singola regola, ottimizzandone l\u2019esecuzione con pi\uf9 regole, combinando diverse misure di similarit\ue0 basate sia su token che su caratteri (e.s. Jaccard Similarity e Edit Distance). Per il GraphJoin ho sviluppato un prototipo Software funzionante e l\u2019ho utilizzato per eseguire esperimenti che dimostrano che la tecnica proposta \ue8 efficace ed \ue8 pi\uf9 efficiente di quelle gi\ue0 esistenti in termini di tempo di esecuzione.Data sources that provide a huge amount of semi-structured data are available on Web as tables, annotated contents (e.g. RDF) and Linked Open Data. These sources can constitute a valuable source of information for companies, researchers and government agencies, if properly manipulated and integrated with each other or with proprietary data. One of the main problems is that typically these sources are heterogeneous and do not come with keys to perform join operations, and effortlessly linking their records. Thus, finding a way to join data sources without keys is a fundamental and critical process of data integration. Moreover, for many applications, the execution time is a critical component (e.g., in finance of national security context) and distributed computing can be employed to significantly it. In this dissertation, I present distributed data integration techniques that allow to scale to large volumes of data (i.e., Big Data), in particular: SparkER and GraphJoin. SparkER is an Entity Resolution tool that aims to exploit the distributed computing to identify records in data sources that refer to the same real-world entity\u2014thus enabling the integration of the records. This tool introduces a novel algorithm to parallelize the indexing techniques that are currently state-of-the-art. SparkER is a Working Software prototype that I developed and employed to perform experiments over real data sets; the results show that the parallelization techniques that I have developed are more efficient in terms of execution time and memory usage than those in literature. GraphJoin is a novel technique that allows to find similar records by applying joining rules on one or more attributes. This technique combines similarity join techniques designed to work on a single rule, optimizing their execution with multiple joining rules, combining different similarity measures both token- and character- based (e.g., Jaccard Similarity and Edit Distance). For GraphJoin I developed a Working Software prototype and I employed it to experimentally demonstrate that the proposed technique is effective and outperforms the existing ones in terms of execution time
Wagenaar G. - One of the best experts on this subject based on the ideXlab platform.
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Artefacts in Agile Team Communication
2019Co-Authors: Wagenaar G.Abstract:Agile Software development (ASD) has become standard in Software development. ASD methods share a preference for face-to-face communication rather than formal comprehensive documentation. As agile teams are independent in their internal processes they can decide on, whether or not, to use documentation artefacts. Their decisions elaborate the Agile Manifesto’s second statement: “Working Software over comprehensive documentation”. Therefore, our main research question is: What is the role of artefacts in communication in agile Software development teams? The hypothesis underlying this research is that artefacts have proven their value in traditional Software development and that agile teams will carry over some of this value to their current practices. In first instance the use of artefacts in agile teams was investigated through interviewing team members in three agile teams in a case study. Its conclusions demonstrated the basic validity of our hypothesis. The teams did use artefacts and we partially confirmed previous findings and complemented them with additional artefacts. Having established the blend of traditional and agile artefacts does not shine light on reasons for using them. In a holistic view we first investigated the existence of a relation between maturity of an agile team’s ASD and its use of artefacts. Evidence was found for maturity to be negatively correlated with the non-agile/all artefacts ratio. In a follow-up study we explicitly investigated rationales for the use of artefacts. In fourteen agile teams interviews were held with prominent team members to discuss the use of artefacts and the motivation for using them. Agile teams stated five groups of rationales, among which one for agile artefacts and four for traditional artefacts, for instance to promote internal team communication. Formal and informal communication in ASD are often regarded as two distinct end points, resembling the distinction between explicit and tacit knowledge. This distinction is recognized not to be a black and white one and this raised the question whether or not agile teams also experience intermediate appearances between formal communication (artefacts) and informal communication (face-to-face). We coined this appearance a ‘fuzzy’ artefact, an artefact which is not formally documented, but one that is still explicitly recognized by an agile team. The findings confirmed their existence and teams use them, for instance, in the requirements process, the elaboration of user stories and in taking Go/ No-Go decisions. The overall answer to our research question thus is: Yes, artefacts play an important role in the communication within agile teams. This is no surprise as far as agile artefacts, artefacts that are inherent to ASD, are involved, for instance a product or sprint backlog, or user stories. Agile teams use, in addition, non-agile artefacts, which are associated with traditional Software development rather than ASD. However, they have sound reasons to do so. Two examples: (1) Team-internal communication benefits from functional and technical design artefacts, (2) Quality assurance leads to test-related artefacts. In general, agile teams are able to explain why they are using the artefacts they use. Artefacts do not replace face-to-face communication, but complement it
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Working Software over comprehensive documentation : Rationales of agile teams for artefacts usage
2018Co-Authors: Wagenaar G., Overbeek S.j., Lucassen G.g., Brinkkemper S., Schneider KurtAbstract:Agile Software development (ASD) promotes Working Software over comprehensive documentation. Still, recent research has shown agile teams to use quite a number of artefacts. Whereas some artefacts may be adopted because they are inherently included in an ASD method, an agile team decides itself on the usage of additional artefacts. However, explicit rationales for using them remain unclear. We start off to explore those rationales, and state our primary research question as: What are rationales for agile teams to use artefacts? Our research method was a multiple case study. In 19 agile teams we identified 55 artefacts and concluded that they in general confirm existing research results. We introduce five rationales underlying the usage of artefacts in ASD: (1) Adoption of ASD leads to agile artefacts, (2) team-internal communication leads to functional and technical design artefacts, (3) quality assurance leads to test-related artefacts, (4) agile teams impose governance on their own activities, and (5) external influences impose user-related material. With our contribution we substantiate the theoretical basis of the Agile Manifesto in general and contribute to the current research with regard to the usage of artefacts in ASD in particular. Agile teams themselves may from this research extract guidelines to use more or less comprehensive documentation
Schneider Kurt - One of the best experts on this subject based on the ideXlab platform.
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Working Software over comprehensive documentation : Rationales of agile teams for artefacts usage
2018Co-Authors: Wagenaar G., Overbeek S.j., Lucassen G.g., Brinkkemper S., Schneider KurtAbstract:Agile Software development (ASD) promotes Working Software over comprehensive documentation. Still, recent research has shown agile teams to use quite a number of artefacts. Whereas some artefacts may be adopted because they are inherently included in an ASD method, an agile team decides itself on the usage of additional artefacts. However, explicit rationales for using them remain unclear. We start off to explore those rationales, and state our primary research question as: What are rationales for agile teams to use artefacts? Our research method was a multiple case study. In 19 agile teams we identified 55 artefacts and concluded that they in general confirm existing research results. We introduce five rationales underlying the usage of artefacts in ASD: (1) Adoption of ASD leads to agile artefacts, (2) team-internal communication leads to functional and technical design artefacts, (3) quality assurance leads to test-related artefacts, (4) agile teams impose governance on their own activities, and (5) external influences impose user-related material. With our contribution we substantiate the theoretical basis of the Agile Manifesto in general and contribute to the current research with regard to the usage of artefacts in ASD in particular. Agile teams themselves may from this research extract guidelines to use more or less comprehensive documentation
Kurt Schneider - One of the best experts on this subject based on the ideXlab platform.
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Working Software over comprehensive documentation – Rationales of agile teams for artefacts usage
Journal of Software Engineering Research and Development, 2018Co-Authors: Gerard Wagenaar, Garm Lucassen, Sietse Overbeek, Sjaak Brinkkemper, Kurt SchneiderAbstract:Agile Software development (ASD) promotes Working Software over comprehensive documentation. Still, recent research has shown agile teams to use quite a number of artefacts. Whereas some artefacts may be adopted because they are inherently included in an ASD method, an agile team decides itself on the usage of additional artefacts. However, explicit rationales for using them remain unclear. We start off to explore those rationales, and state our primary research question as: What are rationales for agile teams to use artefacts? Our research method was a multiple case study. In 19 agile teams we identified 55 artefacts and concluded that they in general confirm existing research results. We introduce five rationales underlying the usage of artefacts in ASD: (1) Adoption of ASD leads to agile artefacts, (2) team-internal communication leads to functional and technical design artefacts, (3) quality assurance leads to test-related artefacts, (4) agile teams impose governance on their own activities, and (5) external influences impose user-related material. With our contribution we substantiate the theoretical basis of the Agile Manifesto in general and contribute to the current research with regard to the usage of artefacts in ASD in particular. Agile teams themselves may from this research extract guidelines to use more or less comprehensive documentation.
Hanna Oktaba - One of the best experts on this subject based on the ideXlab platform.
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productivity in agile Software development a systematic mapping study
International Conference on Software Engineering, 2017Co-Authors: Sandra L Ramrezmora, Hanna OktabaAbstract:Many organizations dedicated to Software development have adopted agile principles with the aim of increasing their productivity. Agile Software Development (ASD) encourages the delivery of Working Software frequently; however, there are many factors that affect productivity in that context. The objective of this paper is to analyze the research works about productivity in ASD to identify: the factors that affect productivity, the most used research methods, the most studied productivity levels, the concepts related to productivity, the most studied agile methodologies, and the use of productivity metrics in ASD. To perform such analysis, we conducted a Systematic Mapping Study (SMS) in which we included 25 primary studies. As result of the SMS, we identified the main factors that affect productivity in ASD and the context in which the included primary studies were performed. We found that Case Study was the most used research method, and interviews and questionnaires are widely used as data collection methods. We found that most of the reported factors affecting productivity in ASD are related to agile teams, and although quality should be related to productivity, it was not considered in all studies.
