scholarly journals Reduction of Search Space in Restful Service Discovery

2019 ◽  
pp. 143-152
Author(s):  
G. Venugopal ◽  
P. Radhika Raju ◽  
A. Ananda Rao
Keyword(s):  
Web Services ◽  
Service Discovery ◽  
Nearest Neighbor ◽  
Search Space ◽  
Unit Testing ◽  
It Services ◽  
K Nearest Neighbor ◽  
Business Services ◽  
Service Unit ◽  
Restful Service

Web Services has been enabled IT services and computing technology to perform business services more efficiently and effectively. REpresentational State Transfer (REST) is to be used for creating Web APIs/services. In the existing system, web service search engines for RESTful Web Services/Api’s provide Keyword, Tag and Semantic based search functions. One of the RESTful service discovery, referred as Test-oriented RESTful service discovery with Semantic Interface Compatibility (TASSIC) have been developed by the search of RESTful Service’s/Api’s. TASSIC approach will search the semantic characteristics of search and match interface terms in the service document. An inability to consider the classification and in finding the suitable Api’s or services are a key issue of the search space in Tassic. A new approach has proposed for reduction of the search space in restful service discovery to develop a k-Nearest Neighbor classification algorithm. it provide candidate services with ranking based on semantic similarity, and classifying of similar candidate services and service unit testing will be considered. This approach is meant for increasing search precision in the retrieval and quick search for classifying their RESTful services or Api according to user-defined criteria.

Experiences in Building Mobile E-Business Services

2009 ◽  
pp. 275-297
Author(s):  
Ivano De Furio ◽  
Giovanni Frattini ◽  
Luigi Romano
Keyword(s):  
Web Services ◽  
Service Discovery ◽  
New Technologies ◽  
Service Oriented Architecture ◽  
Building Blocks ◽  
Service Description ◽  
Semantic Approach ◽  
Business Services ◽  
Business Agility ◽  
Service Oriented

Organizations in all sectors of business and government are pursuing service-oriented architecture (SOA) initiatives in response to their need for increased business agility. This is particularly true for mobile telecommunications companies. That is why mobile telecom operators need to research new and innovative sources of revenue. Innovation is not an easy task. It requires embracing a new way of doing business, where new technologies are fundamental. SOA architecture and Web services technology are proposed by IT industry as the best solution to create a network of partnership and new services, but despite software producer claims, interoperability issues arise with service composition. Such a problem can be significantly reduced by adopting a semantic approach in service description and service discovery. Our research is focused on new methods and tools for building high personalized, virtual e-business services. A new service provisioning architecture based on Web services has been conceived, taking into account issues related to end-user mobility. The following pages deal with a proposal for creating real localized, personalized virtual environments using Web services and domain ontologies. In particular, to overcome interoperability issues that could arise from a lack of uniformity in service descriptions, we propose a way for controlling and enforcing annotation policies based on a Service Registration Authority. It allows services to be advertised according to guidelines and domain rules. Furthermore, this solution enables enhanced service/component discovery and validation, helping software engineers to build services by composing building blocks and provision/deliver a set of personalized services.

Posture labeling based gesture classification for Turkish sign language using depth values

Kybernetes ◽  
2016 ◽  
Vol 45 (4) ◽  
pp. 604-621
Author(s):  
Ediz Saykol ◽  
Halit Talha Türe ◽  
Ahmet Mert Sirvanci ◽  
Mert Turan
Keyword(s):  
Sign Language ◽  
Video Processing ◽  
Visually Impaired ◽  
Nearest Neighbor ◽  
Search Space ◽  
K Nearest Neighbor ◽  
Content Type ◽  
Gesture Classification ◽  
Original Part ◽  
Grid Based

Purpose – The purpose of this paper to classify a set of Turkish sign language (TSL) gestures by posture labeling based finite-state automata (FSA) that utilize depth values in location-based features. Gesture classification/recognition is crucial not only in communicating visually impaired people but also for educational purposes. The paper also demonstrates the practical use of the techniques for TSL. Design/methodology/approach – Gesture classification is based on the sequence of posture labels that are assigned by location-based features, which are invariant under rotation and scale. Grid-based signing space clustering scheme is proposed to guide the feature extraction step. Gestures are then recognized by FSA that process temporally ordered posture labels. Findings – Gesture classification accuracies and posture labeling performance are compared to k-nearest neighbor to show that the technique provides a reasonable framework for recognition of TSL gestures. A challenging set of gestures is tested, however the technique is extendible, and extending the training set will increase the performance. Practical implications – The outcomes can be utilized as a system for educational purposes especially for visually impaired children. Besides, a communication system would be designed based on this framework. Originality/value – The posture labeling scheme, which is inspired from keyframe labeling concept of video processing, is the original part of the proposed gesture classification framework. The search space is reduced to single dimension instead of 3D signing space, which also facilitates design of recognition schemes. Grid-based clustering scheme and location-based features are also new and depth values are received from Kinect. The paper is of interest for researchers in pattern recognition and computer vision.

Composition Oriented Semantic Relationships Mining Framework Research

2014 ◽  
Vol 513-517 ◽  
pp. 470-473 ◽  
Author(s):  
Zheng De Zhao ◽  
Yue Hui Cui ◽  
Jian Jun Li
Keyword(s):  
Web Services ◽  
Service Composition ◽  
Service Discovery ◽  
Search Space ◽  
Experimental Results ◽  
Business Logic ◽  
Semantic Relationships ◽  
Functional Clustering ◽  
Two Stages ◽  
Mining Algorithms

In order to improve the efficiency of service discovery and service composition, this paper proposes a Composition oriented Web services semantic relationships mining framework. Firstly, Web services need to be pretreated, which are filtered based on QoS; and then adopt the method of service functional clustering to generate service classes, which largely reduces the services search space and improve the efficiency of service discovery; Secondly, in order to excavate the semantic relationships between service classes that meet the business logic requirement, we need to set the composition rules between service classes; Finally, using two stages of mining algorithms to excavate the semantic relationships between service classes. Experimental results are given to validate the feasibility and validity of our framework.

Context-Aware Web Service Clustering and Visualization

2020 ◽  
Vol 17 (4) ◽  
pp. 32-54
Author(s):  
Banage T. G. S. Kumara ◽  
Incheon Paik ◽  
Yuichi Yaguchi
Keyword(s):  
Web Services ◽  
Web Service ◽  
Service Discovery ◽  
Search Space ◽  
Context Aware ◽  
Specific Context ◽  
Web Service Discovery ◽  
Domain Specific ◽  
Clustering Approach ◽  
Service Clustering

With the large number of web services now available via the internet, web service discovery has become a challenging and time-consuming task. Organizing web services into similar clusters is a very efficient approach to reducing the search space. A principal issue for clustering is computing the semantic similarity between services. Current approaches do not consider the domain-specific context in measuring similarity and this has affected their clustering performance. This paper proposes a context-aware similarity (CAS) method that learns domain context by machine learning to produce models of context for terms retrieved from the web. To analyze visually the effect of domain context on the clustering results, the clustering approach applies a spherical associated-keyword-space algorithm. The CAS method analyzes the hidden semantics of services within a particular domain, and the awareness of service context helps to find cluster tensors that characterize the cluster elements. Experimental results show that the clustering approach works efficiently.

scholarly journals METODE TRANSFORMASI CAKAR AYAM UNTUK MEREDUKSI SEARCH-SPACE PADA INTRUSION DETECTION SYSTEM BERBASIS K-NEAREST NEIGHBOR

2016 ◽  
Vol 2 (2) ◽  
Author(s):  
Kharisma Muchammad ◽  
Thomas Brian
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Nearest Neighbor ◽  
Detection System ◽  
Search Space ◽  
Group Method ◽  
K Nearest Neighbor ◽  
Pair Group ◽  
Space Reduction ◽  
Unweighted Pair Group Method

[Id] Penggunaan Intrusion Detection System (IDS) pada jaringan komputer merupakan hal yang diperlukan untuk menjaga keamanan jaringan. Beberapa IDS berbasis K-nearest neighbor (KNN) memiliki akurasi yang relatif baik namun jika data training terlalu besar, waktu yang diperlukan untuk mendeteksi serangan juga meningkat. Waktu untuk deteksi bisa ditekan dengan mereduksi search space pada data training. Namun problem reduksi search space dengan mempertahankan kualitas deteksi masih merupakan problem terbuka. Pada artikel ini diajukan suatu metode transformasi "cakar ayam" berbasis jumlah jarak data ke centroid dan jarak data ke dua sub-centroid untuk mereduksi search space pada IDS berbasis K-nearest neighbor. Localized K-nearest neighbor dilakukan pada data yang telah tertransformasi. Eksperimen menggunakan agglome-rative hierarchial clustering dengan Unweighted Pair-Group Method of Centroid pada dataset NSL-KDD 20% menunjukkan penurunan search space maksimum sebesar 38% dengan tingkat akurasi sebesar 77.5%. Tingkat akurasi dan specificity maksimum yang dicapai pada eksperimen sebesar 88% dan 88.3% dengan tingkat reduksi sebesar 12% dan tingkat sensitifity maksimum yang dicapai sebesar 80.2% pada tingkat reduksi 11%. Berdasarkan eksperimen, luas search space dapat dikurangi sambil menjaga akurasi deteksi. Rasio tradeoff antara akurasi dan search space mungkin dapat diperbaiki dengan mengganti algortima clustering dengan divisive hierarchial clustring. Abstract : clustering, deteksi intrusi, keamanan jaringan [En] Intrusion detection System (IDS) for computer network has became an essential needs to ensure network security. Some K-nearest neighbor (KNN) based IDS have a relatively good accracy in detecting attack, but the need to use all training data costs time consumption . Detection time cost can be reduced by reducing search space needed for the algorithm. The problem of search space reduction while maintaining decent accuracy still an open problem. In this Paper we propose a new transformation method "chiken claw" method. which based on sum of two distances. The first distance is the distance of data and its cluster. The later is distance of data to 2 of its cluster's sub-centroid..This method is proposed to reduce the search space on K-nearest neighbor based IDS because the search is based on resulted one dimentional transformed data. Experiment using Unweighted Pair-group Method of centroid on NSL-KDD 20% shows maximum search space reduction 38% with 75% accuracy. Maximum accuracy and sensitivity in the experiment is 88% and 88.3% respectively with space reduction 12%. Maximum sensitivity from experiment is at 80.2% with 11% space reduction. Based on experiments, search space can be reduced while maintaining accuracy. Search space-accuracy trade off might be improved by using different clustering algorithm such as divisive hierarchial clustering

Experiences in Building Mobile E-Business Services

2010 ◽  
pp. 279-301
Author(s):  
Ivano De Furio ◽  
Giovanni Frattini ◽  
Luigi Romano
Keyword(s):  
Web Services ◽  
Service Discovery ◽  
New Technologies ◽  
Service Oriented Architecture ◽  
Building Blocks ◽  
Service Description ◽  
Semantic Approach ◽  
Business Services ◽  
Business Agility ◽  
Service Oriented

Organizations in all sectors of business and government are pursuing service-oriented architecture (SOA) initiatives in response to their need for increased business agility. This is particularly true for mobile telecommunications companies. That is why mobile telecom operators need to research new and innovative sources of revenue. Innovation is not an easy task. It requires embracing a new way of doing business, where new technologies are fundamental. SOA architecture and Web services technology are proposed by IT industry as the best solution to create a network of partnership and new services, but despite software producer claims, interoperability issues arise with service composition. Such a problem can be significantly reduced by adopting a semantic approach in service description and service discovery. Our research is focused on new methods and tools for building high personalized, virtual e-business services. A new service provisioning architecture based on Web services has been conceived, taking into account issues related to end-user mobility. The following pages deal with a proposal for creating real localized, personalized virtual environments using Web services and domain ontologies. In particular, to overcome interoperability issues that could arise from a lack of uniformity in service descriptions, we propose a way for controlling and enforcing annotation policies based on a Service Registration Authority. It allows services to be advertised according to guidelines and domain rules. Furthermore, this solution enables enhanced service/component discovery and validation, helping software engineers to build services by composing building blocks and provision/deliver a set of personalized services.

scholarly journals Service identification using k-NN machine learning

2018 ◽  
Vol 7 (2.4) ◽  
pp. 182
Author(s):  
Travis Joseph Poulose ◽  
S Ganesh Kumar
Keyword(s):  
Machine Learning ◽  
Web Service ◽  
Service Discovery ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
Service Request ◽  
Service Identification ◽  
Daunting Task ◽  
K Nearest Neighbor Algorithm

Web service categorization is a daunting task since it requires semantic descriptions of those services which are not provided to the majority of those websites. The proposal of a Semantic based automated service discovery requires a request from the user that can be analyzed which then provides the user with a list of related webs services based on the request that instigated the search. The problem with these service categorizations listed in the Universal description Discovery and Integration (UDDI) is the way the information is related to one another. The relations follow a syntactic method. Semantic based service descriptions is necessary for accurate web categorization. With the help of machine learning we can also predict the user’s service request automatically based on previous searches and also select the best web service for a particular request that the user has made using a k-nearest neighbor algorithm. By doing this we can distinguish between the various types of user requests, provide services that are suitable for that particular request as well as suggest other services that might potentially suit the needs of the user.  

scholarly journals ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA

2019 ◽  
Vol 1 (1) ◽  
pp. 14-19
Author(s):  
Febrian Wahyu Ramadhan ◽  
Husni Teja Sukmana ◽  
Lee Kyung Oh ◽  
Luh Kesuma Wardhani
Keyword(s):  
Sentiment Analysis ◽  
Test Data ◽  
Nearest Neighbor ◽  
Training Data ◽  
It Services ◽  
K Nearest Neighbor ◽  
Data Indexing ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
F Measure

Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.

ANALYSIS OF WARGANET COMMENTS ON IT SERVICES IN MANDIRI BANK USING K-NEAREST NEIGHBOR (K-NN) ALGORITHM BASED ON ITSM CRITERIA

2019 ◽  
Vol 1 (1) ◽  
pp. 14-19
Author(s):  
Febrian Wahyu Ramadhan ◽  
Husni Teja Sukmana ◽  
Lee Kyung Oh ◽  
Luh Kesuma Wardhani
Keyword(s):  
Sentiment Analysis ◽  
Test Data ◽  
Nearest Neighbor ◽  
Training Data ◽  
It Services ◽  
K Nearest Neighbor ◽  
Data Indexing ◽  
Positive Sentiment ◽  
Negative Sentiment ◽  
F Measure

Sentiment analysis is a method for reviewing products or services to determine opinions or feelings about a product. The results of the analysis can be used by companies as evaluation materials and considerations to improve the products or services provided. This study aims to test the level of public sentiment on the quality of Bank Mandiri services that have received ISO 20000-1 with the application of sentiment analysis using the K-NN algorithm based on ITSM criteria. The initial classification in this study uses the lexicon method by detecting words included in sentiment words, the results of which are included as labels on training data and test data. Formation of the classification with the K-NN algorithm by taking into account the results of the training data indexing and weighting of the test data, with the value of k as the decision maker limit. The trial results of 10 scenarios show that the classification using the K-NN algorithm as a sentiment classification is 98% accuracy value of 50 test data to 600 training data, with 24% getting positive sentiment, 22% negative sentiment and 55% neutral sentiment, with f -measure 95.83%. while in testing 100 the test data obtained 79% accuracy value with 21% getting positive sentiment, 42% negative sentiment and 38% neutral with an f-measure value of 68.42%.

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