Search results

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided Routing (P‐LAR) protocol. Simulation study and analytical results of P‐LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

The purpose of this paper is to review the recent critique of the discovery view of opportunities and by implication the current state of the so‐called creation view of opportunities.

A systematic review of articles published from 2000‐2010 which feature a critique of the discovery view of opportunities. The review uses an open coding approach to identify central themes in the critique. Four central themes are identified, and from within‐theme patterns three distinct groups within the literature are identified.

The four themes suggest that the discovery view is incomplete, that social and relational interactions are more pervading and important than the discovery view suggests, that opportunities are created and that the role of individuality and subjectivity needs to be emphasized more. Three distinct groups within the literature are identified, each presenting different critiques of the discovery view, theoretical foci and implications for method and practice. Furthermore, the discussion suggests that the opportunity concept is a focal point for important debates in the entrepreneurship field, that the creation view is diverse and should not be referred to in the singular and that seeking reconciliation between the discovery and creation views is a problematic strategy. Finally, it is suggested that a continued dialog exploring differences both between the discovery and creation views as well as between the creation views is a fruitful strategy for the development of the field.

The article presents a review of both the critique of the discovery view and the so‐called creation view, thereby supplementing and advancing from existing reviews of the opportunity concept in entrepreneurship. This furthers our understanding of the role of opportunities in entrepreneurial processes.

The purpose of this paper is to investigate users' knowledge and use of social networking sites and folksonomies to discover if social tagging and folksonomies, within the area of independent music, aid in its information retrieval and discovery. The sites examined in this project are MySpace, Lastfm, Pandora and Allmusic. In addition, the ways in which independent record labels utilise social networking sites for promotion are investigated.

Three groups of participants were surveyed using questionnaires. These groups were music concert attendees, people who responded to online postings to social networking sites, and independent record companies. In addition interviews were held with digital music experts.

The results suggest that respondents use social networking sites for music discovery but are not generally aware of folksonomic approaches to music discovery. When users do use and contribute to the folksonomy, most respondents were found to tag for personal retrieval purposes rather than attempting to aid the retrieval purposes of the population of site users as a whole. The four record labels unanimously agreed that social networking sites are having a major impact on independent music discovery. Digital distribution has a major impact on independent record labels. It facilitates discovery but at the same time digital distribution creates new promotional dilemmas.

The project is small scale but the research area is a relatively novel one, and the results are interesting enough to share more generally in the hope that this project will stimulate further research activity in this area.

Entrepreneurship.

The case has been used at Master's level but it has direct application to any MBA programme or entrepreneurship module.

Adrian Gore started Discovery in 1992 with seed-funding of R10 million from merchant banking group, Rand Merchant Bank (RMB), as a health insurance company within the RMB stable. By 2009, Discovery had become a large, listed, financial services institution employing more than 5,000 people and comprising not only Discovery Health (DH), but also Discovery Life (DL), Discovery Invest (DI) and Discovery Vitality (a wellness programme). In addition, it had operations in the USA, where it licensed Vitality for use by employers and other health insurers, and in the UK where it operated two joint ventures with The Prudential plc – Pruhealth and Prulife.

To understand the similarities and differences between corporate and start-up entrepreneurship; to understand the entrepreneurial process within an established organization; to explore the environment within an established company in terms of how much it supports or constrains entrepreneurship; and to look at creative ways to overcome obstacles to entrepreneurship in established companies.

Teaching notes.

The purpose of this paper is to evaluate and compare open source and proprietary discovery tools and find out how much discovery tools have achieved towards becoming the next generation catalog.

The paper summarizes characteristics of the next generation catalog into a check‐list of 12 features. This list was checked against each of seven open source and ten proprietary discovery tools to determine if those features were present or absent in those tools.

Discovery tools have many next generation catalog features, but only a few can be called real next generation catalogs. Federated searching and relevancy based on circulation statistics are the two areas that both open source and proprietary discovery tools are missing. Open source discovery tools seem to be bolder and more innovative than proprietary tools in embracing advanced features of the next generation catalog. Vendors of discovery tools may need to quicken their steps in catching up.

It is the first evaluation and comparison of open source and proprietary discovery tools on a large scale. It will provide information as to exactly where discovery tools stand in light of the much desired next generation catalog.

The main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.

Systematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.

In this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.

This research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.

One of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.

This survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.

The purpose of this paper is to characterise a number of current and future computing environments and summarises their resource discovery requirements. It then seeks to analyse, with respect to the requirements of each environment, several established service discovery protocols and some newer protocols that are still in the research domain. In addition, the key features of a new resource discovery protocol that has been developed to operate with heterogeneous computing environments are described.

A comprehensive literature survey was undertaken, highlighting the shortcomings of existing resource discovery protocols with respect to large pervasive computing environments. Given the identified gaps in existing protocols, an alternative protocol is suggested.

The main findings of this paper relate to the identified shortcomings of existing resource discovery protocols. It was also found that a hybrid resource discovery protocol capable of spanning dynamic, mobile computing environments and more stable ones was able to overcome many of the challenges presented by large‐scale pervasive computing environments.

This paper presents comprehensive literature survey of the state‐of‐the‐art in resource discovery protocols, pointing out some of the problems that are not solved. The paper describes the design of an alternative protocol, and presents an evaluation of it. The pervasive computing research community can draw upon the survey and evaluation to guide the design of future resource discovery protocols for the increasingly dynamic world in which we live.

Price discovery and spillover effect are prominent indicators in the commodity futures market to protect the interest of consumers, farmers and to hedge sharp price fluctuations. The purpose of this paper is to investigate empirically the price discovery and volatility spillover in Indian agriculture spot and futures commodity markets.

This study uses Granger causality, vector error correction model (VECM) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) to examines the price discovery and spillover effects for nine most liquid agricultural commodities in spot and futures markets traded on National Commodity and Derivatives Exchange (NCDEX).

The VECM results show that price discovery exists in all the nine commodities with futures market leading the spot in case of six commodities, namely soybean seed, coriander, turmeric, castor seed, guar seed and chana. Whereas in case of three commodities (cotton seed, rape mustard seed and jeera), price discovery takes place in the spot market. The Granger causality tests indicate that futures markets have stronger ability to predict spot prices. Supporting these, the results from EGARCH volatility test reveal that there exist mutual spillover effects on futures and spot markets. Thus, it could be inferred that futures market is more efficient in price discovery of agricultural commodities in India.

These results can help the market participants to benefit by hedging out the uncertainty and the policymakers to design futures contracts to improve the efficiency of the agricultural commodity derivatives market.

The findings provide fresh view on lead–lag relationship between future and spot prices using the latest data confirming that futures market indeed is dominant in price discovery.

There are very few studies that have explored the efficiency of the agricultural commodity spot and futures markets in India using both price discovery and volatility spillover in a detailed manner, especially at the individual agriculture commodity level.

The purpose of this study is to examine shifts in the volume and complexity of reference questions received at a small liberal arts college following the implementation of a Web-scale discovery service.

Researchers used the Warner model of reference classification to review and classify reference questions from the academic year prior to the implementation of EBSCO Discovery Service (EDS) and the academic year following EDS implementation to evaluate the change in volume of questions and complexity.

Research findings are significant as they document a 34 per cent decline in overall volume of reference activity following the integration of a Web-scale discovery service into the research process. Notably, the question category rated as the highest level of reference complexity (level IV) registered an 18.5 per cent increase in volume, post-EDS implementation. Question levels I-III all saw declines in volume ranging from 45 to 14 per cent.

Limitations of the study relate to multiple librarians having recorded questions as they were received by reference services and, as a result, some variation in transaction description should be expected. The concurrent deployment of a Web-scale discovery service and an integrated link resolver made delineation of the respective impact of each tool difficult.

Libraries contemplating the integration of a Web-scale discovery service into the research process will be able to use the research analysis to contemplate service redesign in advance of discovery implementation. The research results support additional training of reference personnel to service higher levels of in-depth inquiries. A redesign of reference services incorporating a tiered reference model using proactive chat with referrals to library faculty for in-depth research consultations is recommended.

After a literature review of relevant research, the researchers discovered few similar studies. As a result, this analysis will be of significant value to the library profession.