A theory of data-intensive software services

Ma, Hui; Schewe, Klaus‐Dieter; Thalheim, Bernhard; Wang, Qing

doi:10.1007/s11761-009-0051-x

Cited by 16 publications

(12 citation statements)

References 27 publications

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“…Unfortunately, they have not yet led to the creation of widely accepted industrial technologies [2]. Some of the researchers used a multi-stage optimized stock forecast model to grasp the changing trend of the stock market [7]. The market mechanism combined with software agents adjusts demand and supply through interaction among agents using prices as common information [6].…”

Section: Literature Reviewmentioning

confidence: 99%

Green Referential Data Base for the Indian Stock Market

Singh¹,

Dimri²,

Chakraborty³

2014

IJCA

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A traditional database methodology has been used in the Indian stock market. Forecasting of the market is not only based on the prices of the stocks but also on other integrated information like socio-economic factors, prices, politics etc. Changing data format and its behavior requires a new methodology to handle and integrate. Investments solely depend upon efficiency and accuracy of the data. Data required for the stock market decision making process is generated from every event and all events have its own impact on the market. Irrespective of the nature of the event and its format, market requires data integration of all kind. Because of acute scarcity of natural resources, processing of the stock market data requires green methodologies which contribute to save energy, power, time, space etc. Fractal behavior of the market shows repetition of the stock prices again and again. Large amount of space, time, power etc have been utilized to store and process these repetitive data. Referential data base is one of the answers to this problem. This paper proposes referential data base for the stock market prices without compromising efficiency and accuracy for market forecasting methods.

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Section: Literature Reviewmentioning

confidence: 99%

Green Referential Data Base for the Indian Stock Market

Singh¹,

Dimri²,

Chakraborty³

2014

IJCA

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“…Concretely, the similarity between two subgraphs is minimized, while the similarity within each subgraph is maximized (line 1). When is split to multiple subclusters, we remove and related edges from the cluster network (lines 5-10), and add the generated subclusters with related edges into the cluster network (lines [11][12][13][14][15][16][17][18][19][20][21][22][23]. Note that the weights on the edges between two DP service vectors in a cluster are updated in lines 4-15 of Algorithm 2.…”

Section: : End Formentioning

confidence: 99%

“…DP services are common nowadays [5]- [7]. Besides data-driven Web services, DP services can also be services built upon data centers, 2 Grid data sources [8], scientific services [9], pervasive and location-based data services [10], [11], data access service [12], [13], real-time data services [14], and even data correlation between Web pages [15]. Different from EP services which are usually modeled by means of input, output, preconditions, and effects [16], DP services have no preconditions and do not bring effects.…”

Section: Introductionmentioning

confidence: 99%

Data Providing Services Clustering and Management for Facilitating Service Discovery and Replacement

Zhou

Sellami

Gaaloul

et al. 2013

IEEE Trans. Automat. Sci. Eng.

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In service-oriented computing, a user usually needs to locate a desired service for: (i) fulfilling her requirements or (ii) replacing a service, which disappears or is unavailable for some reasons, to perform an interaction. With the increasing number of services available within an enterprise and over the Internet, locating a service online may not be appropriate from the performance perspective, especially in large Internet-based service repositories. Instead, services usually need to be clustered according to their similarity. Thereafter, services in one or several clusters are necessary to be examined online during dynamic service discovery. In this paper, we propose to cluster data providing (DP) services using a refined fuzzy -means algorithm. We consider the composite relation between DP service elements (i.e., input, output, and semantic relation between them) when representing DP services in terms of vectors. A DP service vector is assigned to one or multiple clusters with certain degrees. In addition, we introduce some operations for managing service clusters, when new services emerge or existing services disappear or become unavailable. When grouping similar services into one cluster, while partitioning different services into different clusters, the capability of service search engine is improved significantly. We have prototyped our approach and the source code is freely available on the web. We have evaluated our clustering approach in different settings and the results are very promising.Note to Practitioners-DP services are common nowadays, and there are a huge number of DP services in the Internet-scale environment. Locating an appropriate DP service accurately and efficiently with respect to the requirements of a certain requestor is a challenging task. This work presents a novel clustering-oriented approach, which aims to group DP services into clusters offline leveraging the semantic description of these DP services, and provides operations for managing these clusters. The clustering procedure is conducted by means of a machine learning technique which is a refined fuzzy -means method. A DP service can be assigned to one or several clusters with certain degrees. DP service clusters constitute a cluster network. Operations are provided for facilitating the evolution of cluster network, when a DP service emerges or disappears sometime for some reason. A prototype has been implemented and the approach has been evaluated on two test collections of DP services considering factors impacting the efficiency. Our method can be readily used in industrial DP service repository for supporting DP service discovery and replacement.

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“…ASSs abstract from and generalise our research on Web Information Systems [14], and integrate the customised ASM thesis for database transformations [15]. ASSs were introduced in [11], and are described in detail in [12] including their composition.…”

Section: Introductionmentioning

confidence: 99%

“…In Section III we briefly present our model of ASSs. For details, however, we have to refer to [12]. Section IV is then devoted to clouds as service federations, i.e.…”

Section: Introductionmentioning

confidence: 99%

An abstract model for service provision, search and composition

Schewe²,

Wang

2009

2009 IEEE Asia-Pacific Services Computing Conference (APSCC)

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Service-oriented computing, cloud computing, and web services composition mark cornerstones of a paradigm shift in software systems engineering. The general new idea is to use as much as possible services that are made available by others, mostly disseminated via the web. In this paper, we present an abstract model for clouds as federations of services together with a specification of semantics and quality characteristics. For the services as such we adopt the abstract model of abstract state services, which is based on views on some hidden database layer that are equipped with service operations. For the semantics we adopt types for in-and output, pre-and post-conditions, and a description of functionality within an operations ontology. In addition, quality characteristics capture performance, costs, availability, etc. On the basis of this model of clouds, users may conduct a (web) search for usable services, extract service components, and recompose these components. The quality characteristics can be used to optimise the selection of usable services.

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A theory of data-intensive software services

Cited by 16 publications

References 27 publications

Green Referential Data Base for the Indian Stock Market

Green Referential Data Base for the Indian Stock Market

Data Providing Services Clustering and Management for Facilitating Service Discovery and Replacement

An abstract model for service provision, search and composition

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