다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

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A Break in the Clouds: Towards a Cloud Definition

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

The Object Management Group's (OMG) Model Driven Architecture (MDA) strategy envisages a world where models play a more direct role in software production, being amenable to manipulation and transformation by machine. Model Driven Engineering (MDE) is wider in scope than MDA. MDE combines process and analysis with architecture. This article sets out a framework for model driven engineering, which can be used as a point of reference for activity in this area. It proposes an organisation of the modelling 'space' and how to locate models in that space. It discusses different kinds of mappings between models. It explains why process and architecture are tightly connected. It discusses the importance and nature of tools. It identifies the need for defining families of languages and transformations, and for developing techniques for generating/configuring tools from such definitions. It concludes with a call to align metamodelling with formal language engineering techniques.

Model Driven Engineering

Managing an organization requires access to information in order to monitor activities and assess performance. Trying to understand what information an organization has can be challenging because the information systems collect and process vast amount of data in various forms. To flow in the running stream of rapidly changing, increasingly competitive global market scenario and increasingly volatile consumer and market behavior and rapidly shortening product life cycles, business enterprises today are necessary to analyze accurate and timely information about financial operations, customers, and products using familiar business terms, in order to gain analytical insight into business problems and opportunities. Enterprises are building business intelligence systems that support business analysis and decision making to help them better understand their operations and compete in the marketplace. This paper describes the life cycle comprising various phases in the development of a BI system. The paper elaborates the implementation issues of BI in an organization focusing a case study

Business intelligence systems: design and implementation strategies

Web service composition concerns the building of new value added services by integrating the sets of existing web services. Due to the seamless proliferation of web services, it becomes difficult to find a suitable web service that satisfies the requirements of users during web service composition. This paper systematically reviews existing research on QoS-aware web service composition using computational intelligence techniques (published between 2005 and 2015). This paper develops a classification of research approaches on computational intelligence based QoS-aware web service composition and describes future research directions in this area. In particular, the results of this study confirms that new meta-heuristic algorithms have not yet been applied for solving QoS-aware web services composition.

Computational Intelligence Based QoS-Aware Web Service Composition: A Systematic Literature Review

With the growing demand and commercial availability of cloud services, the need for comparison of their functionality against different prices and performance has arisen. A relevant and fair comparison is still challenging due to diverse deployment options and dissimilar features of different services. This paper addresses a hybrid multi-criteria decision-making model involving the selection of cloud services among the available alternatives. The proposed methodology assigns various ranks to cloud services based on the quantified quality-of-service parameters using a novel extended Grey Technique for Order of Preference by Similarity to Ideal Solution integrated with analytical hierarchical process. Further, we analyse the proposed cloud service selection method in terms of sensitivity analysis, adequacy under change in alternatives, adequacy to support group decision-making, and handling of uncertainty. This analysis helps both researchers and practitioners for analysing more fruitful approaches for cloud service selection.

SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services

QoS-aware cloud service composition using eagle strategy

Mashups combine data and services provided by third parties through open APIs (such as Google Maps and Flickr), as well as internal data sources owned by users. The creation of mashups is supported by a complex ecosystem of interconnected data providers, mashup platforms, and users. In this paper, we examine the structure of the mashup ecosystem and its growth over time. Several observations follow from our analysis. First, we can conclude that while the number of new APIs and mashups over time follows a linear growth pattern, the distribution of mashups over APIs is not uniform but follows a power law. This implies that a small number of APIs provides the basis for the majority of mashups, and the other APIs are only used in certain application niches. Second, our analysis suggests that mashup platforms were introduced in response to the increasing complexity of mashups, as mashups evolved from one-feature mashups (widgets). Third, we observe that complementary relationships between open APIs are formed based on the position of the APIs in the ecosystem. The propensity of two APIs to be used together in the same mashup depends on the existing number of mashups to which they both contribute. The growth of the mashup ecosystem follows a pattern where keystone data providers or ‘powerful hubs’ attract niche data providers as complementors, and the positions of keystones in the ecosystem are mutually reinforcing.

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G. R. Gangadharan

Papers

Business intelligence systems: design and implementation strategies

Computational Intelligence Based QoS-Aware Web Service Composition: A Systematic Literature Review

SELCLOUD: a hybrid multi-criteria decision-making model for selection of cloud services

QoS-aware cloud service composition using eagle strategy

Modeling the mashup ecosystem: structure and growth