Web service clustering using multidimensional angles as proximity measures

Platzer, Christian; Rosenberg, Florian; Dustdar, Schahram

doi:10.1145/1552291.1552294

Cited by 100 publications

(65 citation statements)

References 18 publications

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“…The data set is formed by collecting all the information about the given data and the data is identified by the characteristics, similarity based on functionality and description, then after the comparison process the clustered data set is formed once the cluster of data is emulated the advanced collaborative clustered automated filter is used to filter out the unwanted content information [18] [19]. The data set is formed by giving out the unique id and value for each data unit, based on the characteristics and similarities and also it look for the user preferences in framing the clustered data unit which is a pile in our proposed system.…”

Section: Data Set Formationsmentioning

confidence: 99%

“…Before the content based filtering begins it accepts dataset formed in the process and with the hierarchy of the user preference the recommended data set are formulated and the ranking will be given based on the user preferences [18]. In a content-based system, keywords are used to describe the items, besides the user profile is built to indicate the keyword item to which the user specified their desires.…”

Section: Efficiency Of the Proposed Systemmentioning

confidence: 99%

“…If the number of the target service's neighbours reaches to the maximum value, then its worst case time complexity of item-based prediction is O (n k ). Since k n n  and k m m  , the cost of computation will decrease significantly [18]. In order to evaluate the efficiency of proposed recommender system, the online computation time of proposed recommender system is compared with that of traditional recommender system, as shown in Figure 5.…”

Section: Computation Time For the Proposed Recommendationmentioning

confidence: 99%

See 2 more Smart Citations

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Manjula¹,

Chilambuchelvan²

2016

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Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.

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Section: Data Set Formationsmentioning

confidence: 99%

Section: Efficiency Of the Proposed Systemmentioning

confidence: 99%

Section: Computation Time For the Proposed Recommendationmentioning

confidence: 99%

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An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Manjula¹,

Chilambuchelvan²

2016

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“…Caesars pays fanatical attention-typically through human observation-to ensuring that its most loyal customers don't wait in lines. With video analytics on big data tools, it may be able to employ more automated means for spotting service issues involving less frequent customers [15]. Caesars is also beginning to analyze mobile data, and is experimenting with targeted real-time offers to mobile devices.…”

Section: Example Of Big Data For New Offeringsmentioning

confidence: 99%

Big Data Analytics using Hadoop

Dhyani¹,

Barthwal²

2014

IJCA

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This paper is an effort to present the basic understanding of BIG DATA is and it's usefulness to an organization from the performance perspective. Along-with the introduction of BIG DATA, the important parameters and attributes that make this emerging concept attractive to organizations has been highlighted. The paper also evaluates the difference in the challenges faced by a small organization as compared to a medium or large scale operation and therefore the differences in their approach and treatment of BIG DATA. A number of application examples of implementation of BIG DATA across industries varying in strategy, product and processes have been presented. The second part of the paper deals with the technology aspects of BIG DATA for it's implementation in organizations. Since HADOOP has emerged as a popular tool for BIG DATA implementation, the paper deals with the overall architecture of HADOOP alongwith the details of it's various components. Further each of the components of the architecture has been taken up and described in detail.

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“…Thus, we obtain automatically an efficient ranking of the services retrieved. 7 http://www.cs.princeton.edu/ blei/ctm-c/index.html We propose also to use an other approach based on the proximity measure called Multidimentional Angle (also known as Cosine Similarity); a measure which uses the cosine of the angle between two vectors [20], [7]. In the first time, we represent the user's query as a distribution over topics.…”

Section: B a Probabilistic Topic Model Approachmentioning

confidence: 99%

Correlated Topic Model for Web Services Ranking

Aznag¹,

Quafafou²,

Jarir³

2013

IJACSA

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Abstract-With the increasing number of published Web services providing similar functionalities, it's very tedious for a service consumer to make decision to select the appropriate one according to her/his needs. In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors from web service descriptions. In our approach, topic models are used as efficient dimension reduction techniques, which are able to capture semantic relationships between word-topic and topic-service interpreted in terms of probability distributions. To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering and ranking web services using latent factors. In our experiment, we evaluated our Service Discovery and Ranking approach by calculating the precision (P@n) and normalized discounted cumulative gain (NDCGn).

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Web service clustering using multidimensional angles as proximity measures

Cited by 100 publications

References 18 publications

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Big Data Analytics using Hadoop

Correlated Topic Model for Web Services Ranking

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