Search

In the article methodical recommendations on the use of web analytics system in food industry enterprises, its structure and services are suggested.

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.

In recent years, Web service discovery has been a hot research topic. In this paper, we propose a novel Web services discovery approach, which can mine the underlying semantic structures of interaction interface parameters to help users find and employ Web services, and can match interfaces with high precision when the parameters of those interfaces contain meaningful synonyms, abbreviations, and combinations of disordered fragments. Our approach is based on mining the underlying semantics. First, we propose a conceptual Web services description model in which we include the type path for the interaction interface parameters in addition to the traditional text description. Then, based on this description model, we mine the underlying semantics of the interaction interface to create index libraries by clustering interaction interface names and fragments under the supervision of co-occurrence probability. This index library can help provide a high-efficiency interface that can match not only synonyms but also abbreviations and fragment combinations. Finally, we propose a Web service Operations Discovery algorithm (OpD). The OpD discovery results include two types of Web services: services with “Single” operations and services with “Composite” operations. The experimental evaluation shows that our approach performs better than other Web service discovery methods in terms of both discovery time and precision/recall rate.

Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web’s largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

In the domain of emergent event analysis, it is still a difficult issue to acquire the event information from the Web efficiently. To solve the problem, this paper proposes a crowdsensing-based Web crawler for emergent event analysis. When an emergent event occurs, some web users post event information on the Web with geographical position. These web users can be regarded as crowd sensors. In the proposed method, the crawler takes advantage of the information from these crowd sensors, such as semantic information, geographical information, sentiment information, etc., to get the information of event efficiently. Experimental results show that the proposed method can improve the efficiency of crawlers when compared with universal crawlers both in the period of sparse information and in the period of eruptible information.

A detailed study on the use of Semantic Web technologies in CBSE.Advancement is being made from using ontological concepts to Linked Data in CBSE.Semantic web techniques help in addressing the various challenges that CBSE faces.Simple and freely accessible CBSE applications that employ Semantic Web are limited.Some open issues relevant to the topic in concern have been examined. With the advent of Component-based software engineering (CBSE), large software systems are being built by integrating pre-built software components. The Semantic Web in association with CBSE has shown to offer powerful representation facilities and reasoning techniques to enhance and support querying, reasoning, discovery, etc. of software components. The goal of this paper is to research the applicability of Semantic Web technologies in performing the various tasks of CBSE and review the experimental results of the same in an easy and effective manner. To the best of our knowledge, this is the first study which provides an extensive review of the application of Semantic Web in CBSE from different perspectives. A systematic literature review of the Semantic Web approaches, employed for use in CBSE, reported from 2001 until 2015, is conducted in this research article. Empirical results have been drawn through the question-answer based analysis of the research, which clearly tells the year wise trend of the research articles, with the possible justification of the usage of Semantic Web technology and tools for a particular phase of CBSE. To conclude, gaps in the current research and potential future prospects have been discussed.

This paper provides an overview of the emergence of artificial intelligence in accounting and auditing. We discuss the current capabilities of cognitive technologies and the implications these technologies will have on human auditors and the audit process itself. We also provide industry examples of artificial intelligence implementation by Big 4 accounting firms. Finally, we address some potential biases associated with the creation and use of artificial intelligence and discuss implications for future research.

For describing and understanding the real world, location is an important factor. Consequently, it also appears in many Web applications and mining approaches as a crosscutting issue. LocWeb 2016 continues a workshop series addressing issues at the intersection of location-based services and Web architecture and was held at WWW 2016. It combines geospatial search, information management, and Web architecture, with a main focus on location-aware information access. The workshop drew contributions from various fields, ranging from mobility analytics over new ways to understand cities to Web standards. LocWeb2016 had an interdisciplinary combination of contributions, with two keynotes and three long papers. We will briefly discuss the workshop theme and the contributions.

This paper briefly summarizes the progress of artificial intelligence (AI) and web intelligence (WI) in the last two decades. The reason why we mention AI and WI together is because those have strong relationship with each other. This paper first summarizes the history of AI, and then gives brief description of supervised learning, which I think has played a major role in AI in the last two decades. As most history of WI is in the target decades, this paper first briefly describes major WI topics, and then gives more detailed description about information recommendation, which I think one of more successful and necessary technologies in practical use.

This paper provides various PHP programming framework (CakePHP2, CodeIgniter, Symfony2, Yii and PhalconPHP) popularity overview and comparison using various criteria. Based on the data obtained during the study two frameworks were selected for deeper analysis - Symfony2 and PhalconPHP. This article offers a description of the architecture and main features of selected frameworks (routing, template engine, etc.). During framework comparison a performance test was developed with a goal to determine performance and effectiveness of frameworks during the same task. For performance testing a Ticket Reserving System cashier list section was chosen. Tests were performed using ab.exe (Apache Benchmark) tool that comes with the Apache Web server. Based on the comparison results recommendations are made that allow Web developers to choose a framework for creating a real-world Web project. It is planned to expand the developed Web system capabilities using Web services that are based on open standards and protocols (SOAP, XML-RPC, REST, etc.).

With the advancement of Information Technology, web is growing rapidly and it has became necessary part of our daily lives. It is mandate to study the navigation behavior of the user to improve the quality of web site design for personalization and further recommendation. Analysis of web navigation behavior heavily relies on navigational models. This paper is an effort to give insights of current state-of-the-art techniques used for web navigation prediction. These navigation models are broadly classified into three categories: sequential mining, classification and clustering. Analytical analysis is performed on all the categories used in web navigation prediction. Further empirical analysis is performed on popular techniques of each category Markov Model (sequential mining), Support vector machine (classification) and K-means (clustering) on the common platform to measure the effectiveness of these techniques.

This paper proposes a context-aware semantic planning graph technique for Web services composition. We first use an ontology based context model for extending Web services descriptions with information about the most suitable context for its use. Then, we transform the composition problem into a semantic context aware graph planning problem to build a set of best composed Web services based on user’s context. The construction of the planning graph is based on semantic context-aware Web service discovery. This allow, for each step of the construction, to add most suitable Web services in terms of semantic compatibility between the services parameters, and their context similarity with the user’s context. In the backward search step, semantic and contextual similarity scores are used to find composed Web services list. Finally, in the ranking step, a score is calculated for each candidate solution and a set of ranked solutions is returned to the user.

Classifying web pages is to automatically assign predefined class to them. It is one of the main applications of web mining. The authors' aim is to detect the targeted nation based on the web pages content. It is an original application. In this paper, the authors propose different web mining approaches using machine learning algorithms such as Naïve Bayes and Support Vector Machine in order classify them. They present detailed stages of the procedure. The best experimental result based on an original corpus created by their own means shows a very attention grabbing f-score of 85%.

Customer satisfaction is the key secret of success for all industries regardless of whether it is web enabled or not. This paper focuses the role of web mining in achieving a viable edge in business. Web mining is becoming the tool for success for those who adopt electronic means of operation for conducting their business. Web mining is the application of data mining techniques to discover patterns from the Web through content mining, structure mining, and usage mining. Web mining can contribute to a large extent in gaining a competitive advantage in business. Business goals should be well understood.

Service-Oriented Computing promotes building applications by consuming and reusing Web Services. However, the selection of adequate Web Services given a client application is still a major challenge. The effort of assessing and adapting candidate services could be overwhelming due to the “impedance” of Web Service interfaces expected by clients versus the actual interfaces of retrieved Web Services. In this work, we present a novel structural-semantic approach to help developers in the retrieval and selection of services from a service registry. The approach is based on a comprehensive structural scheme for service Interface Compatibility analysis, and WordNet as the semantic support to assess identifiers of operations and parameters. We also empirically analyze, compare and contrast the performance of three service selection methods: a pure structural approach, a pure semantic approach, and the structural-semantic (hybrid) approach proposed in this work. The experimental analysis was performed with two data-sets of real-world Web Services and a service discovery support already published in the literature. Results show that our hybrid service selection approach improved effectiveness in terms of retrievability of Web Services compared to the other approaches. Fil: Garriga, Martín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: de Renzis, Alan Ismael. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: Lizarralde, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Flores, Andrés Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina Fil: Cechich, Susana Alejandra. Universidad Nacional del Comahue. Facultad de Informatica; Argentina Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina

With the popularization of the Internet, permeation of sensor networks, emergence of big data, increase in size of the information community, and interlinking and fusion of data and information throughout human society, physical space, and cyberspace, the information environment related to the current development of artificial intelligence (AI) has profoundly changed. AI faces important adjustments, and scientific foundations are confronted with new breakthroughs, as AI enters a new stage: AI 2.0. This paper briefly reviews the 60-year developmental history of AI, analyzes the external environment promoting the formation of AI 2.0 along with changes in goals, and describes both the beginning of the technology and the core idea behind AI 2.0 development. Furthermore, based on combined social demands and the information environment that exists in relation to Chinese development, suggestions on the development of AI 2.0 are given.

For the upcoming conference on Intelligence Studies at ICI 2020 in Bad Nauheim, Germany the focus of this issue of JISIB is on collective intelligence and foresight. The first two papers by Søilen and Almedia and Lesca deal with collective intelligence from an intelligence studies perspective. It may be said that the Internet itself is a gigantic collective intelligence effort, the largest in human history. Open source is a prerequisite for this system to work for everyone. The article by Černý et al. is on open source. All other contributions are on the connection between the Internet, software and intelligence. This issue consists of seven articles to compensate for two articles that were taken out by editors in the last issue. The first article by Søilen entitled “Making sense of the collective intelligence field: a review” is a historical review of the field of collective intelligence. The paper shows how collective intelligence is an interdisciplinary field and argues there is a flaw in the notion of “wisdom of crowds”. Collective intelligence can be understood in terms of social systems theory and as such this approach has been fruitful for the social sciences, although so far not very popular. It also bares relevance for the study of business and economics. The second article by Almeida and Lesca is entitled “Collective intelligence process to interpret weak signals and early warnings”. Early warning and the detection of weak signals is a vital topic for any intelligence organization. Two aspects are discussed in the paper, the importance of new technology and collective sense making or interpretation The third article by Shaikh and Singhal entitled “Study on the various intellectual property management strategies used and implemented by ICT firms for business intelligence” deals with intellectual property rights and patenting strategies. The authors identify a number of defensive and offensive IP strategies applied to ICT companies. The results have a bearing on patent acquisitions. The fourth article by Lamrhari et al. is entitled “Web intelligence for understanding customer satisfaction: application of Latent Dirichlet Allocation (LDA) and the Kano model”. Customer satisfaction today is mostly measured with data from the internet, using different business intelligence techniques. The Kano model is still valuablei,ii, but the way we gather information to assess the different levels in the model has changed. The authors use Latent Dirichlet Allocation to analyze the voice of customer (VOC) in online reviews. They suggest that BI techniques and a fuzzy-Kano model can enable companies to better understand their customers’ online reviews. The fifth article by Nahili et al. is entitled “A new corpus-based convolutional neutral network for big data text analysis”. Companies need efficient ways to analyze everything that is said about them on the internet (reviews, comments). The paper suggests a convolutional neural network (CNN) as it has been successfully used for text classification. IMDB movie reviews and Reuters datasets were used for the experiment. The sixth article by Černý et al. is entitled “Using open data and google search data for competitive intelligence analysis”. Taking the Czech antidepressant market as an example, the authors show how competitive intelligence can be obtained using Google Search data, Google Trend and other OSINT sources. The seventh article by Dadkhah et al. is entitled “The potential of business intelligence tools for expert findings”. The paper suggests a way for researchers to find experts using business intelligence tools. The same method may also be used by any business or person looking for experts on a specific topic. As always, we would above all like to thank the authors for their contributions to this issue of JISIB. Thanks to Dr. Allison Perrigo for reviewing English grammar and helping with layout design for all articles and to the Swedish Research Council for continuous financial support. We hope to see you all at the ICI 2020 on the 16-17 March, 2020. The deadline for the two-page abstract submission is March 1st, 2020.

Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products’ aspects along with customers’ sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4 % precision.

It is our great pleasure to welcome you to the Web4City 2019 1st International Workshop on Web Intelligence and Smart Cities. Web4City emphasizes on the contribution of artificial intelligence (AI) and especially web intelligence in smart cities. Specifically, the focus is on the emerging role of intelligence that transforms the smart services to adaptive and self-evolving ones, which deal with the usual smart city challenges like local growth, quality of life's improvement, efficiency and climate change etc. Issues like local growth evolve with AI and Industry 4.0; transportation transforms with autonomous vehicles and platform-based services; environment is continuously being monitored; living is being supported by numerous types of smart services that can transform with AI (e.g., e-learning, e-banking, e-commerce, e-tourism etc.); people interact with the machines; while governance transforms to real-time government. Our call received 7 articles from Europe and Asia, which followed a peer review process regarding their relevance, quality, and novelty and 3 full and 1 short paper were selected. Review and acceptance statistics are as follows: The beginning of this half-day event contains the following talk: Then, a panel of presenters covers topics with regard to: We expect that you will find Web4City's program interesting, which will enable you to learn and share ideas with other researchers and practitioners from institutions around the world.

The challenge in today’s corporations is that even though the technology portfolio of a company plays a crucial role in delivering revenue—falling as a topic mainly under the area of technology management—technology may have a negative image due to observed risks or failing the sustainability criteria. It may influence the company’s image and brand image, possibly also influencing decisions at corporate level. The monitoring of technology sentiments is therefore emphasized, benefiting from the advanced methods for business environment scanning, namely market and competitor intelligence functions. This paper utilizes a new big data based method, mostly utilized in market(MI)/competitor intelligence(CI) functions of the company, opinion mining, to analyse the global media sentiment of nuclear power and projects deploying the technology. With this approach, it is easier to understand the linkage to corporate images of companies deploying the technology and also related corporate decisions, mainly done in the areas of technology market deployment, marketing and strategic planning. The results indicate how the media sentiment towards nuclear power has been mostly negative globally, particularly in social media. In addition, results from similar analyses from a single company’s images for the companies currently deploying the technology are seemingly less negative, indicating the influence of company’s communication and branding activities. This paper has implications showing that a technology’s media sentiment can influence a company’s brand image, marketing communications and the need for actions when technology is deployed. In conclusion, there seems to be a need for better co-operation between different corporate functions, namely technology management, MI, marketing and strategic planning, in order to indicate technology image impacts and also counteract firestorms from social media.

BREXIT is the single biggest geopolitical event in British history since WWII. Whilst the political fallout has become a tragicomedy, the political ramifications has had a profound impact on the Pound and the FTSE 100 index. This paper examines Twitter political discourse surrounding the BREXIT withdrawal agreement. In particular we focus on the discussions around four different exit strategies known as “Norway”, “Article 50”, the“Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of rumblings of the cancellation of the Meaning Vote on December 10th 2018 inclusive of second defeat on the Prime Minister’s BREXIT exit strategy on February 14th to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis is then introduced to investigate the hypothesis that BREXIT political and public sentiment, as measured by the twitter sentiment time series, is indicative of changes in the GBP/EUR Fx and FTSE 100 Index. Our results indicate that the accuracy of the “Article 50” scenario had the single biggest effect on short run dynamics on the FTSE 100 index, additionally the “Norway” BREXIT strategy has a marginal effect on the FTSE 100 index whilst there was no significant causation to the GBP/EUR Fx.

From the very early days, and due to its unprecedented opportunities and advantageous facilities, the Web has been revolutionizing almost every aspects of human life, technology, etc. As a dynamic phenomenon, the Web itself has experienced several major evolution stages. Such fundamental changes never thought to abolish predecessor technologies and concepts but taking them to a new level of functionality. In fact, these paradigm shifts are necessary and inevitable in order to take advantages of state of the art technologies and approaches. In this regard, social Web, semantic Web and similar nomenclatures aimed to emphasize the key idea that best represents the latest achievements and possibly future directions. Thanks to many years of research and development, the current state (version) of the Web puts forward some outstanding opportunities both to make the most of available resources/capabilities and establish the foundation of the future Web. Specifically, convergence of human intelligence (of Web users), machine intelligence (of Web agents) and intelligence of things (through Web of Things) toward shaping Web of Intelligence (WoI) can be regarded as a breakthrough in the field. The major contribution of this work is proposing a Web-based conceptual model for intelligence harvesting (i.e. Web of Intelligence) through which humans, machines and things could collaborate to solve large scale intelligence-intensive problems in a more efficient and effective way. The rationale behind this idea, its building blocks and related considerations are delineated in this paper. Moreover, a novel application based on the proposed concept, entitled comprehensive intelligent search, is introduced.

The importance of effective and timely communication is critical in disaster management life cycle. With the proliferation of communication and web technologies, the challenge has now shifted from the availability of information to the efficient handling of the sheer amount of information available online. This has attracted researchers and practitioners to find ways which can facilitate individuals and organizations in their decision making while dealing with large amounts of online data. This chapter presents (1) the evolution of web technologies from Web 1.0 to Web 3.0, (2) the overview of communications tasks involved in disaster management, and (3) the literature survey on the pros and cons of Web 2.0 and Web 3.0 in disaster management. By comparing the role of Web 2.0 with Web 3.0, the chapter also attempts to explore how the communication tasks of disaster management could be improved using Web 3.0. It is anticipated that the findings of this chapter will assist the decision makers to use Web 3.0 as a strategic tool for effective communication in disaster management.

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

The development of urbanization and improvement of intelligent urban areas need better procedures for planning of urban zones. In urban communities with old structures, residents invest an excess of energy doing dreary and futile exercises like holding up in lines, heading out long separations to purchase products or get benefits, and being stuck in roads turned parking lots. There are different issues consider as air contamination, ecological issues, old structures, nonstandard urban foundations, and media transmission frameworks. To adapt to present circumstances, a city needs savvy frameworks and parts including a keen economy, shrewd transportation, brilliant condition, brilliant natives, shrewd way of life, and organization. To plan such frameworks and parts in a savvy city, there ought to be an instrument which can process the put away information and give the resultant data to the administration and clients. In such manner, information mining and Web insight are compelling devices which have a noteworthy job in structuring a shrewd city and preparing huge information. At that point keen parts, the foundations of a smart city, and the job of information mining in building up an urban city are examined subsequent to displaying ideas and definitions.

General Purpose Technologies, or GPTs are defined in the economic literature as the key technologies that shape the economy. Despite the large conceptual literature base on Blockchain potential to revolutionize the current economic system, there is a lack of empirical research on its economic nature and the course of technological development. The paper at hand covers this research gap by providing the quantitative approach aimed at understanding the evolutionary path of Blockchain and its scope for improvement – an acknowledged feature of a GPT - in line with the industrial dynamics and GPT literature. The longitudinal analysis of Blockchain-related patents from PATSTAT and their rule-based classification both from technological and application perspectives is complemented by the study of Blockchain media landscape to provide insights into the social context in which it emerges. The increasing amount of patents addressing essential technical issues, such as security, scalability, and usability contribute to wider adoption of the technology, whereas the positive sentiment in the media associated with Blockchain creates beneficial social context for its development. The empirical results advance the claim that Blockchain does show a positive scope for improvement peculiar to the GPTs in the making and, therefore, deserves attention as a technology that will define macroeconomic dynamics in a long term.

In many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data analytics—and specially, association rule mining or web data mining—is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships—in the form of association rules—among frequently occurring patterns. For instance, in IEEE/WIC/ACM WI 2016 and 2017, serial and parallel algorithms were proposed to find interesting web pages. However, like most of the existing association rule mining algorithms, these two algorithms also were not designed for mining big data. Moreover, the search space of web pages can sparse in the sense that web pages are connected to a small subset of all web pages in the search space. In this paper, we present a compact bitwise representation for web pages in the search space. Such a representation can then be used with a bitwise serial or parallel association rule mining system for web mining and recommendation. Evaluation results show the effectiveness of our compression and the practicality of our algorithm—which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them—in real-life web applications.

Computerization is currently a key element in everybody’s life – individuals, businesses, travel sectors, medical services and many more. As the world is pushing ahead, the web and innovation have been the implications that these areas use to contact individuals and get benefit from the brand or administration they advance. The private protection area is perceived as one of the quickest developing ventures. This quick development has powered unfathomable changes over the previous decade. These days, most valuable resource protective items exist, such as homes, vehicles, gems, life, etc. All insurance agencies are at the forefront in taking leading tasks, measures and numerical models in order to increase benefits by adapting customer claims. Customary techniques based only on human-in-the-loop models are extremely tedious and incorrect. In this paper we are establishing a safe and computerised protection framework that reduces human communication, ensures safeguards, alerts and illuminates hazardous customers, distinguishes misleading cases and reduces money-related unhappiness. The proposed Framework for Vehicle Protection Strategies is a single-page online application that aims to establish an entirely practical, free framework to monitor vehicle insurance records. It is designed to provide such insurance agencies with an online stage for exact preparation, coordinated information, production recovery and record capacity.

There are numerous search engines available in today's world to search and retrieve the required information. However retrieval of meaningful and appropriate formation as per the user requirement is always a challenging task. The foremost intention of any search engine is to provide the information with in a quick span of time. Since the nature of data available in World-Wide-Web shows heterogeneity in common and the sources of data are also distinct with each other, issues pertaining to schema structure and data representational are also there. In such circumstances, to eliminate inconsistencies and for enabling seamless integration of multiple data sources while retrieving web data, an efficient web search mechanism that fulfils the customer requirement is always needed. To enable the integration of multiple data sources while performing efficient retrieval of web data, an intelligent web search framework has been proposed in this paper.

With the emergence of the Programmable Web paradigm, the World Wide Web is evolving into a Web of Services, where data and services can be effectively reused across applications. Given the wide diversity and scale of published Web services, the problem of service discovery is a big challenge for service-based application development. This is further compounded by the limited availability of intelligent categorization and service management frameworks. In this paper, an approach that extends service similarity analysis by using morphological analysis and machine learning techniques for capturing the functional semantics of real-world Web services for facilitating effective categorization is presented. To capture the functional diversity of the services, different feature vector selection techniques are used to represent a service in vector space, with the aim of finding the optimal set of features. Using these feature vector models, services are classified as per their domain, using ensemble machine learning methods. Experiments were performed to validate the classification accuracy with respect to the various service feature vector models designed, and the results emphasize the effectiveness of the proposed approach.

present word web is huge storage of information and it will keep increasing with growing of internet technologies. But the human being capability to read, access and understand content does not increase with that tread. Hence it becomes complex to website owners to present proper information to the users. This led to provide personalized web services to users. One of the well-known approaches in providing web personalization is Web Usage Mining. In this paper, our motive of web usage mining is to discover users' access patterns of web pages automatically and quickly from the huge sever access log records, such as frequently visited hyperlinks, frequently accessed web pages and users grouping. Also, we proposed a new method for discovering users' access patterns and recommend it to the user.

world wide web expanded day by day ,many website (avg 51 million website)added to the web every year. Almost all organization support open data and make their data available over the web, which increase innovation. The Semantic Web is an evolution and extension of the existing Web that allows computers to manipulate data and information Semantic web is based on the content oriented description of digital documents with standardized vocabularies that provide machine understandable semantics .Basic building block for Semantic web are Ontology ,RDF/OWL ,SPARQL. Semantic web vocabulary can be considered as a special form of ontology. Semantic web provide connection between human and computer by making the computer think more like a human ,It is artificial intelligence which can intelligently learn and understand the semantic. . Semantic web is also understand by Web 3.0 which is the executable and read/write Web. The idea of the Semantic Web is still undergoing research and development.

Nowadays web sites have become an important means for communication. Every application is converted to internet based application. A huge amount of the data is added daily and huge amount of information is accessed from the web. The millions to billions of web users are accessing the interested data from the web making the network traffic. To overcome these problems, recommender systems which are the part of machine learning are introduced. At present, recommender systems play an important role in the ecommerce field. The shopping sites where the users are recommended with the interested products and resources based on their navigation behavior, profile and their interest on the products. The recommender systems are basically divided into three types, they are content based, collaborative and hybrid recommender systems. As the web users are increasing the network performance is affected. Thus it is required to enhance the performance of the network by using the mining and machine learning classification techniques. In this paper, we propose an efficient web recommender system, which uses the concept of preprocessing, clustering technique and expectation maximization naive bayes as predictive model.