On Evaluating Commercial Cloud Services: A Systematic Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons

D4.6.1.1 Report on ontology mediation for case studies v.1

WP4 Ontology Mediation ReportThe aim of this deliverable is to identify the requirements for mediation for the SEKT casestudies. The data sources from each case study are investigated together with the relationships between them and with the scenarios in which two or more of these data sources are used in conjunction, i.e. where data integration is needed. The requirements for mediation are identified based on these scenarios. We should note that as a result of our analysis we identified the opportunity of some architectural changes for two of the casestudies. The new data source landscapes proposed together with guidelines about different mediation approaches should serve as a pillar for the further development of thecase studies. Also the identified requirements show that the main mediation functionality on which the tools developed by the WP4 should focus on is ontology alignment

A model-driven method for the systematic literature review of qualitative empirical research

This paper explores a model-driven method for systematic literature reviews (SLRs), for use where the empirical studies found in the literature search are based on qualitative research. SLRs are an important component of the evidence-based practice (EBP) paradigm, which is receiving increasing attention in information systems (IS) but has not yet been widely-adopted. We illustrate the model-driven approach to SLRs via an example focused on the use of BPMN (Business Process Modelling Notation) in organizations. We discuss in detail the process followed in using the model-driven SLR method, and show how it is based on a hermeneutic cycle of reading and interpreting, in order to develop and refine a model which synthesizes the research findings of previous qualitative studies. This study can serve as an exemplar for other researchers wishing to carry out model-driven SLRs. We conclude with our reflections on the method and some suggestions for further researc

Innovating in the music industry : Blockchain, Streaming & Revenue Capture

Music industry revenues experienced a boom when digital music became available and the music business reinvented itself. Even with the existence of piracy, the business has been growing since 2015. A lot of the credit goes to streaming platforms that introduced the notion of access instead of ownership as the dominant business model (BM). However, the main financial beneficiaries are the platforms, labels, and publishers (the middlemen), whereas musicians receive little from streaming their creations. This is due to an outdated royalty distribution system that was applied to the new BM. This study aims to propose an updated BM by innovating the industry’s payment framework with the use of blockchain technology (BT). It would create a fair and transparent accounting system as well as promoting trust for all professionals in the system. Secondary data on strategic innovation, the music industry, and business model innovation were analyzed in this study. Qualitative and quantitative primary data was collected through a survey and semi-structured interviews conducted with industry professionals. Results revealed that BT could be a game-changer in the way the industry accounts for and pay royalties. However, there is little knowledge about the use of BT so no immediacy to bring about its implementation. Also, the industry lacks incentives to change the BM as big players run the show leaving musicians without little agency to bring about change. Finally, the study concludes that even though BT is a possible solution, the industry might not yet be accepting of this kind of change.As receitas da indústria da música passaram por um boom quando a música digital ficou disponível. O negócio se reinventou. Mesmo com pirataria, o mercado vem crescendo desde 2015. Grande parte se deve às plataformas de streaming que introduziram um modelo de negócios (MN) de acesso, em vez de propriedade que dominava o mercado. No entanto, os principais beneficiários financeiros são as plataformas, gravadoras e editoras (os intermediários), enquanto os músicos recebem pouco de streaming. Isso se deve ao sistema de distribuição de royalties desatualizado que foi aplicado ao novo MN. Este estudo tem como objetivo propor um MN atualizado, através da inovação do sistema de pagamento com o uso de tecnologia blockchain (TB). Esta criaria um sistema contábil justo e transparente, além de melhorar a confiança dos profissionais no sistema. Dados secundários sobre inovação estratégica, indústria da música e inovação em MN foram analisados . Os dados primários qualitativos e quantitativos foram coletados por meio de pesquisa e entrevistas semiestruturadas realizadas com profissionais do setor. Os resultados revelam que a TB pode mudar o jogo em relação a contabilização e pagamento de royalties. No entanto, há pouco conhecimento sobre o uso, portanto não há imediatismo por parte da indústria para implementa-la. Além disso, a indústria carece de incentivos para mudar o MN, já que grandes players conduzem o show, deixando músicos sem alternativas para promover mudanças. Por fim, o estudo conclui que, embora seja uma possível solução para o problema, o setor ainda não aceita esse tipo de mudança

A Model-Driven Method for the Systematic Literature Review of Qualitative Empirical Research

This paper explores a new model-driven method for systematic literature reviews (SLRs), for use where the empirical studies found in the literature search are based on qualitative research. SLRs are an important component of the evidence-based practice (EBP) paradigm, which is receiving increasing attention in information systems (IS) but has not yet been widely-adopted. We illustrate the model-driven approach to SLRs via an example focused on the use of BPMN (Business Process Modelling Notation) in organizations. We discuss in detail the process followed in using the model-driven SLR method, and show how it is based on a hermeneutic cycle of reading and interpreting, in order to develop and refine a model which synthesizes the research findings of qualitative studies. This study can serve as an exemplar for other researchers wishing to carry out model-driven SLRs. We conclude with our reflections on the method and some suggestions for further research

SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval

Traditionally, sparse retrieval systems relied on lexical representations to retrieve documents, such as BM25, dominated information retrieval tasks. With the onset of pre-trained transformer models such as BERT, neural sparse retrieval has led to a new paradigm within retrieval. Despite the success, there has been limited software supporting different sparse retrievers running in a unified, common environment. This hinders practitioners from fairly comparing different sparse models and obtaining realistic evaluation results. Another missing piece is, that a majority of prior work evaluates sparse retrieval models on in-domain retrieval, i.e. on a single dataset: MS MARCO. However, a key requirement in practical retrieval systems requires models that can generalize well to unseen out-of-domain, i.e. zero-shot retrieval tasks. In this work, we provide SPRINT, a unified Python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval. The toolkit currently includes five built-in models: uniCOIL, DeepImpact, SPARTA, TILDEv2 and SPLADEv2. Users can also easily add customized models by defining their term weighting method. Using our toolkit, we establish strong and reproducible zero-shot sparse retrieval baselines across the well-acknowledged benchmark, BEIR. Our results demonstrate that SPLADEv2 achieves the best average score of 0.470 nDCG@10 on BEIR amongst all neural sparse retrievers. In this work, we further uncover the reasons behind its performance gain. We show that SPLADEv2 produces sparse representations with a majority of tokens outside of the original query and document which is often crucial for its performance gains, i.e. a limitation among its other sparse counterparts. We provide our SPRINT toolkit, models, and data used in our experiments publicly here at https://github.com/thakur-nandan/sprint.Comment: Accepted at SIGIR 2023 (Resource Track

The mediating effect of brand trust on the relationships between dimensions of brand equity and purchase intention toward smartphone

It has been stated that the technology of smartphone greatly affects the behavior of people and their attitude toward the purchase. However, there are lack of studies on the purchase intention of customer regarding smartphone usage among young adults has been reported by several researchers at particular in Malaysia. Thus, the current study investigates the relationships between brand equity dimensions namely, brand awareness, perceived quality, brand association and brand loyalty on behavior intention to purchase the smartphone brands. Moreover, this study also explores the mediation effect of brand trust on the relationship between brand equity elements and purchase intention towards smartphone brand in Malaysia. The main purpose of this study was to investigate the mediating effect of brand trust (BT) on the relationship between brand awareness (BAW), perceived quality (PQ), brand association (BAS),brand loyalty (BLO), and purchase intention (PI) of smartphone brands in Malaysia. The findings of the study showed evidence of the significant and positive relationship between PQ, BLO, and PI; while BAW and BAS have insignificant relationship. The results also presented that BAS, PQ, and BLO have positive effect on PI, while BAW has insignificant influence. The results further support the positive relationship between BT and PI. Interestingly, the findings of the research further show that BT mediates the relationship between BAS,BLO, and PI. This empirical study provided fruitful implications to marketers by making significant contributions to the brand management. It also contributes to new knowledge on the existing body of brand management literature by systematically exploring the influence BAW, PQ, BAS, BT, and BLO on PI of smartphone brands in Malaysia. Marketers should improve brand quality, and enhance awareness which may encourage customers to purchase the smartphone brand

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance