97 research outputs found

    Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation

    Get PDF
    Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment. © 2014 Ding et al

    Database recovery

    Get PDF
    Recovery techniques are an important aspect of database systems. They are essential to ensure that data integrity is maintained after any type of failure occurs. The recovery mechanism must be designed so that the availability and performance of the system are not unacceptably impacted by the recovery algorithms running during normal execution. On the other hand, enough information must be stored so that the database can be restored or transactions backed out in a reasonable amount of time. Concepts, techniques, and problems associated with database recovery will be presented in this thesis. The recovery issues for both centralized and distributed systems will be discussed, along with the tradeoffs of different recovery tools. The database recovery schemes in IMS/VS, DB2 and SDD-1 will be described to show approaches in existing systems

    REVIEW OF MICROSCOPIC MODEL FOR TRAFFIC FLOW

    Get PDF
    ABSTRACT Today, the problem of cities urban transportation is becoming something we have to face in our daily life. In Indonesia, traffic congestion is increasingly serious. Several economic and social motivations can be related to the need to minimize the time spent in vehicles for transportation and consequently their related pollution problems. Due to these motivations, the literature on traffic phenomena is already vast and characterized by contributions covering modeling aspects, statement of problems, qualitative analysis, and particularly developed simulation generated by applications. This paper will provides a several literature review of microscopic model based on their utilities, including the critical review about the modeling approaches. Furthermore, some practical issues such as potential for future model improvement using existing and emerging data collection technologies is identified based on Indonesian traffic characteristics and will be presented as a contribution from this paper

    Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions

    Full text link
    Cloud-based enterprise search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient search solutions over their enterprise big datasets. However, individuals and businesses that deal with confidential big data (eg, credential documents) are reluctant to fully embrace such services, due to valid concerns about data privacy. Solutions based on client-side encryption have been explored to mitigate privacy concerns. Nonetheless, such solutions hinder data processing, specifically clustering, which is pivotal in dealing with different forms of big data. For instance, clustering is critical to limit the search space and perform real-time search operations on big datasets. To overcome the hindrance in clustering encrypted big data, we propose privacy-preserving clustering schemes for three forms of unstructured encrypted big datasets, namely static, semi-dynamic, and dynamic datasets. To preserve data privacy, the proposed clustering schemes function based on statistical characteristics of the data and determine (A) the suitable number of clusters and (B) appropriate content for each cluster. Experimental results obtained from evaluating the clustering schemes on three different datasets demonstrate between 30% to 60% improvement on the clusters' coherency compared to other clustering schemes for encrypted data. Employing the clustering schemes in a privacy-preserving enterprise search system decreases its search time by up to 78%, while increases the search accuracy by up to 35%.Comment: arXiv admin note: text overlap with arXiv:1908.0496

    A scalable approach for content based image retrieval in cloud datacenter

    Get PDF
    The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops

    The Influence of Frequency Containment Reserve Flexibilization on the Economics of Electric Vehicle Fleet Operation

    Full text link
    Simultaneously with the transformation in the energy system, the spot and ancillary service markets for electricity have become increasingly flexible with shorter service periods and lower minimum powers. This flexibility has made the fastest form of frequency regulation - the frequency containment reserve (FCR) - particularly attractive for large-scale battery storage systems (BSSs) and led to a market growth of these systems. However, this growth resulted in high competition and consequently falling FCR prices, making the FCR market increasingly unattractive to large-scale BSSs. In the context of multi-use concepts, this market may be interesting especially for a pool of electric vehicles (EVs), which can generate additional revenue during their idle times. In this paper, multi-year measurement data of 22 commercial EVs are used for the development of a simulation model for marketing FCR. In addition, logbooks of more than 460 vehicles of different economic sectors are evaluated. Based on the simulations, the effects of flexibilization on the marketing of a pool of EVs are analyzed for the example of the German FCR market design, which is valid for many countries in Europe. It is shown that depending on the sector, especially the recently made changes of service periods from one week to one day and from one day to four hours generate the largest increase in available pool power. Further reductions in service periods, on the other hand, offer only a small advantage, as the idle times are often longer than the short service periods. In principle, increasing flexibility overcompensates for falling FCR prices and leads to higher revenues, even if this does not apply across all sectors examined. A pool of 1,000 EVs could theoretically generate revenues of about 5,000 EUR - 8,000 EUR per week on the German FCR market in 2020.Comment: Preprint, 23 pages, 21 figures, 10 table

    Impact of operation strategies of large scale battery systems on distribution grid planning in Germany

    Get PDF
    Due to the increasing penetration of fluctuating distributed generation electrical grids require reinforcement, in order to secure a grid operation in accordance with given technical specifications. This grid reinforcement often leads to over-dimensioning of the distribution grids. Therefore, traditional and recent advances in distribution grid planning are analysed and possible alternative applications with large scale battery storage systems are reviewed. The review starts with an examination of possible revenue streams along the value chain of the German electricity market. The resulting operation strategies of the two most promising business cases are discussed in detail, and a project overview in which these strategies are applied is presented. Finally, the impact of the operation strategies are assessed with regard to distribution grid planning.Postprint (author's final draft

    Dynamic hedging strategies: an application to the crude oil market

    No full text
    International audienceThis article analyses long-term dynamic hedging strategies relying on term structure models of commodity prices and proposes a new way to calibrate the models which takes into account the error associated with the hedge ratios. Different strategies, with maturities up to seven years, are tested on the American crude oil futures market. The study considers three recent and efficient models respectively with one, two, and three factors. The continuity between the models makes it possible to compare their performances which are judged on the basis of the errors associated with a delta hedge. The strategies are also tested for their sensitivity to the maturities of the positions and to the frequency of the portfolio rollover. We found that our method gives the best of two seemingly incompatible worlds: the higher liquidity of short-term futures contracts for the hedge portfolios, together with markedly improved performances. Moreover, even if it is more complex, the three-factor model is by far, the best

    Blockchain and sustainable supply chain management in developing countries

    Get PDF
    Theoretical, empirical and anecdotal evidence suggests that there are more violations of sustainability principles in supply chains in developing countries than in developed countries. Recent research has demonstrated that blockchain can play an important role in promoting supply chain sustainability. In this paper we argue that blockchain’s characteristics are especially important for enforcing sustainability standards in developing countries. We analyze multiple case studies of blockchain projects implemented in supply chains in developing countries to assess product quality, environmental accounting and social impact measurement. We have developed seven propositions, which describe how blockchain can help address a number of challenges various stakeholders face in promoting sustainable supply chains in developing countries. The challenges that the propositions deal with include those associated with an unfavorable institutional environment, high costs, technological limitations, unequal power distribution among supply chain partners and porosity and opacity of value delivery networks
    • …
    corecore