Mashup Tool for Automatic Query Generation for Data Web

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1 Mashup Tool for Automatic Query Generation for Data Web K Annapurna Madhavi 1, Prof.S.V.Achutha Rao 2 1 Pursuing M.Tech(CSE), Vikas College of Engineering and Technology, Nunna, Vijayawada.Affliated to JNTUK, Kakinada, A.P., India 2 S.V. Achuta Rao is working as a Professor & Head, Department of CSE at Vikas College of Engineering and Technology, Nunna, Vijayawada, India. Abstract : In real world we know the people who don t know about that system are called as computer illiterates these people doesn t know about the system and applications if any application that is giving some services to them that they don t know about the Query language of application which can help to application in providing service and here if anything that they want extra other than that service then they need to face problem and to overcome this problem we are proposing this solution that provides the custom defined services in this proposed approach We present a query formulation language (called MashQL) in order to easily query and fuse structured data on the web. Here it is very usefull for the people who are not having at having technical knowledge and here they are having the option to get their own service by creating the custom services by them own. Mashups are applications that aggregate functionality, presentation, and/or contents from existing sources to create a new application. Contents are usually generated either using web feeds or an application programming interface (API). Both approaches have limitations as web feeds do not provide powerful data models for complex data structures and lack powerful features of database systems. On the other hand, API s are usually limited to a specific application thus requiring different implementations for each of the sources used in the mashups. We propose a query based aggregation of multiple heterogeneous data sources by combining powerful querying features of XQuery and SPARQL with an easy interface of a mashup tool for data sources in XML and RDF. Our mashup editor allows for automatic generation of mashups with an easy to use visual interface. Index Terms Query Formulation, Semantic Web, Data Web, RDF, SPARQL, Indexing Methods, Query Optimization, Mash up. I-INTRODUCTION The amount of structured and semi-structured data available on the internet has been steadily increasing and many companies are now providing their data publicly accessible through API s, querying interfaces, RESTful web services, or data services. The rapid growth of Web 2.0 technologies has motivated many big companies to make their contents reusable for the creation of new applications using existing data. Many publicly accessible API s such as Google Maps3, Amazon4 and DBPedia5 are available for the users to generate their own new applications using their existing contents. A typical example of such a scenario is the combination of the list of hotels in a particular city with Google Maps to generate an interactive map of hotels or data collected from several news sites and merged together to provide a single access point to the user. Mashups are web applications that consume the available data from third parties and combine/reuse them to build a new application. Mostly the contents are in the form of web feeds or API s. All the contents are combined either on client side using clientside scripts or on server-side using some available server-side technology such as ASP. JSP, etc. Mashups are different from traditional web applications because they are usually dynamically created to serve a very specific and short lived task. Several mashup editors have been launched to encourage people to build new applications using the massive amount of publicly available contents. Yahoo Pipes6, Google Mashups7 and IBM Mashup Center8 are a few examples of the popular mashup editors. However, the limitation of existing mashup editors is that they focus only on web feeds or API s. These web feeds can represent simple information but lack the capability to represent or query data items provided by querying interfaces or data services. On the other hand, API s are usually limited to a specific application thus requiring different implementations for each of the sources used in the mashups. Currently, the development of data mashups to deal with complex data structures requires strong programming skills making mashups hard to create for novice users. We utilize the concept of data mashups and use it to dynamically integrate heterogeneous web data sources by using the extension of XQuery proposed in. All the available data sources over the internet are considered as a huge database and each data ISSN: Page 2805

2 source is considered as a table. Data mashups can generate queries in extended XQuery syntax and can execute the sub-queries on any available data source contributing to the mashup. XML and RDF are the prevailing data formats for web data sources. To query these data sources, one can use XQuery and SPARQL their respective query languages. The novelty of our tool is that it integrates the powerful features of database querying into a data mashup tool. It provides an easy to use interface of a mashup editor to generate complex queries visually for the integration of a multitude of distributed autonomous, and heterogeneous data sources. Mashups demonstrate a more programmatic, workflow-like integration approach, complementary to the query- and search-based data integration approaches. In fact, mashups are massively built on the idea of reusing and combining existing services so that they can also use existing search engines and query services. However, current mashups are mostly very simple and do not yet exploit the full potential of workflowlike data integration, e.g. as needed for enterprise applications or to analyze larger sets of web data. Hence, we see the need for a more powerful workflow-like data fusion approach which preserves mashup features like Web2.0 GUIs, support for reuse and fast development. Providing such an approach incurs several challenges, including the definition of an architecture supporting mashups for integration at three levels, i.e. data, application, and presentation level. Furthermore, a powerful workflow and programming model is needed supporting the execution of existing web services and generic services (or operators) for information extraction, entity search, database queries, and object matching. The set of usable services and data sources should be listed and semantically described in a metadata repository similar as proposed in. A limiting factor for interactive mashups is runtime. Hence, techniques are needed to solve more complex integration tasks, e.g. involving query, search and object matching of larger datasets, within a short time. Here we are going to present an efficient and effective tool for Query formulation language and it is called as MashQL. The standerd of MashQL can be compared by that fact that it combines all the assumption which was already implemented to provide a effiecient Query formulation tool.mashql is a not a interface, it is a language and it assumes that data should be schema free will be a core idea behind design and development of MashQl. We taken web as a Database and each data is in the form of table. Here we assume there is no loss of generalityin context of web data. Hence we can say that in this point of scenario MashQl become a query which have involvement with number of database. For illustrating MashQl we took RDF as a resource for quering it because RDF is the most primitive data model. When our technique will apply on this RDF successfully we can map other data model like XML and many more on it. In case of Relational database we will choose the SQL for querying the data and here we are inviting the MashQL for getting the user intended search and here the new thing we are adding is that reducing the user burden and providing efficient service to the end user. II-MashQL SYSTEM ARCHITECTURE A mashup application comprises three major components, which are data level, process level, and presentation level. The data level is mainly concerned with accessing and integrating heterogeneous web data sources. These sources can provide structured, semi-structured or unstructured data. Existing data mashup tools cannot deal with structural and semantic diversities of heterogeneous data sources. Recently, the importance of using data mashups for data integration using database oriented mashups has been realized. Inspired by Yahoo pipes, there are a few attempts such as MashQL and Deri Pipes to generate semantic queries from data mashups. However, to the best of our knowledge, there exists no data mashup tool which allows the user to formulate queries over web data sources using their respective query languages and at the same time deals with the heterogeneity of the data sources. Our tool is similar to MashQL and Deri Pipes, but we focus on the XQuery extension of with additional support of the SPARQL query language. Using our approach, existing data integration support for mashups is further enhanced to formulate a single query containing inside subqueries of different query languages to deal with heterogeneous data integration. The algorithm does not require the data to contain specific information or tags, bellow screenshots represents the procedure of a query formulation scenario. MashQL Algorithm: ISSN: Page 2806

3 user is book for download and the book is having lot of properties i.e book name, author, publications etc.. we are providing a subject that is book name which is main constraint of the query if the no of books are more with that name by differed in author and publications so it again make a sub-constraint for getting accurate results and here the user chooses the next thing that is author of a book again it displays the different kinds of authors for that book and different types of publications of that book in this way the required object can get accurately by the end user. The above screen shots represents the algorithm and here the description of algorithm is following Step1: Here in step 1 if any end user wants a book for download this is the main object he wants and the subject we are providing in different ways Step2: Here we are going to provide an another option for taking his object with out giving all credentials that he wants and here this subject is called periodical subject Step3: Here again we are doing same thing as like in step2 and here we are adding a filter for filtering the object step by step Step4: Here the way we are getting our object like a pot and crow story here also we are doing same thing the benefit behind this algorithm is that an end user doesn t know about the object that either it exist in database or not so it annoying to him to search by all his required credentials and also it is a time consuming process here by doing like this end user feel free and also accurate results will come as an object. Step5: Finally an end user gets an effective and accurate results of the objective what he wants. Example description : Here in the above example the five steps are illustrated as follows and here the Query formulation is done by taking both subject and object of a query into consideration. here in our example the main object of an end The main window is divided into three panels, namely data source selection, mashup editor, and query results. Data Source Selection. All available/registered data services will be shown in the left panel. Data services can be arranged in different categories based on their functionality as shown in Figure in the left top corner. This categorization is based on metadata provided while registering a data source. Mashup Editor. The central panel in Figure is the mashup editor. The user can select any data service from the list and can easily drag and drop it into the mashup editor. Each data service describes its available data source, its functionality and schema (if provided) which help the user to select the most suitable data service. Once data sources have been selected, several data operations can be applied to generate the results. The operations can be selected from the list shown in the left bottom corner of Figure. The mashup editor has both a design view and a querying interface for generating mashups. The design view provides an easy graphical interface while the querying interface is used by an expert user to write queries in the extended XQuery syntax described in. Queries Generation. Once the user has selected the data items required from a particular data source, data results can be constructed by dragging and dropping the selected items into the result box. An extended XQuery expression will be generated with subqueries in SPARQL or XQuery depending on the type of the selected data sources, the mashup editor after integrating XML and RDF data sources. Hence, the query contains a SPARQL query as a sub-query inside XQuery. For the creation of the queries from the graphical interface we use the approach. ALGORITHM DEMO FOR $a i n doc ( h t t p : / / WISIRISFuzzySearch / Li c e n s e. xml ) / agreement, $b in SPARQLQuery ( SELECT?Availability? ExecutionTime WHERE {? x <http : / / org / 2001/sub # avai l >? Availability? x <http : / / / 2001/sub #QoS>? ExecutionTime ISSN: Page 2807

4 } RETURN <Result> <Service Title >{$a / t i t l e }</ Service Title> <Requirement>{$a / requirement }</Requirement> <Availability >{$b / availability }</ Availability > <ExecutionTime>{$b/ExecutionTime}</ExecutionTim e> </Result> III-IMPLEMENTATION Implementation of MashQL is proposed by an online server side Query maker. Here we took an example of Query generator. Query generation means automatic generation of query on server when a end user place some data it automatically going to convert into most specific query. For implementing this our system should have no of functionality. Some of the basic functionality we are going to describe below. 1-The first one is MashQL language component. 2-The presentation part either user interface for end user. 3-Automatic background queries dispatching in order to support MashQl when it communicate with RDF databases. 4-Translator module which translate formulated MashQl query to SPARKAL and submit it for execution and the formulated MashQl query stored into XML. 5- Resulting Module which should represent the outcome after retrieval or merging of the submitted SPARKAL query.according to need MashQL query also can be published or customized.. System Model : Fig- Implementation scenario of MashQL When we implemented our MashQl architecture some of the issues came across,we are going to discuss about those issue here itself. A. The first one and major issue in implementation of MashQl is URI Normalisation. RDF data having unknowingly URI which can inelegant the MashQL queries.thus it is responsibility of ours that we should have to normalized the MashQL. The normalization technique is based on the common namespace repository. Ones those RDF do not belongs to that namespace, than we have to normalized these RDF for making our MashQl resultant Query elegant. But it is not totally sure that query coming after URI normalization should be elegant, it also can inelegant. B. The second issue in implementation of MashQL is verbalization.this issue is used for improving the elegancy of mashql.here we use verbalize or edit mode.in this when a user moves the mouse over a restriction, that user will get the edit modeand rest of the restriction that point of time going to verbalise. This phenomena made redability of query much closer to naturel language. And it also help out the user to what they searching for and what they actually needed. C. Third issue in implementation of MashQL is Scalable set. When we quering large database by using our MashQl architecture the normal Drop Down list becoming unscalable. Here we developed a scalable list which is more effective and efficient than all previous one.this Scalable list support search, auto complete and ranked based or int the order of ascending or descending. This list also having properties of scrolling. This List firstly showing top ISSN: Page 2808

100 result which are scrollable and next 100 to 200 after clicking on next button and those are also scrollable. SparqlMotion: http://www.topquadrant.

The language-design and the performance complexities of MashQL are fundamentally tackled.

5 100 result which are scrollable and next 100 to 200 after clicking on next button and those are also scrollable. SparqlMotion: We proposed a query formulation language, called MashQL. We have specified four assumptions that a Data Web query language should have, and shown how MashQL implements all of them. The language-design and the performance complexities of MashQL are fundamentally tackled. We have designed and formally specified the syntax and the semantics of MashQL, as a language, not merely a single-purpose interface. We have also specified the query formulation algorithm, by which the complexity of understanding a data source (even it is schema-free) are moved to the query editor. We addressed the challenge of achieving interactive performance during query formulation by introducing a new approach for indexing RDF data. We presented two different implementation scenarios of MashQL and evaluated our implementation on two large datasets. Yahoo Pipes: Abadi D, Marcus A, Madden S, Hollenbach K: Scalable semantic web data management using vertical partitioning. VLDB, Athanasis N, Christophides V, Kotzinos D: Generating On the FlyQueries for the Semantic Web. ISWC2004. BEA Systems, Inc.: BEA AquaLogic Data Services Platform - AUTHORS PROFILE IV-CONCLUSION We provide a database oriented mashup tool for integrating heterogeneous data sources with a visual interface which allows for an easy definition of complex data mashups. This tool can be used as plug-in for web applications to generate powerful and efficient data integration mashups.in this proposed system of MashQL we used SPARKAL technique. We have designed and formally specified the syntax and the semantics of MashQL, as a language, not merely a singlepurpose interface. We have also specified the query formulation algorithm, by which the complexity of understanding a data source (even it is schema-free) are moved to the query editor. We introduced here by introducing a new approach for indexing RDF data. We presented two different implementation scenarios of MashQL and evaluated our implementation on two large datasets.this technique is behave as a language better than the interface. K Annapurna Madhavi, Pursuing M.Tech(CSE) from Vikas College of Engineering and Technology, Nunna, Vijayawada. Affiliated to JNTU-Kakinada, A.P., India Prof S.V.Achutha Rao, is working as an HOD of CSE at Vikas College of Engineering and Technlogy, Nunna, Vijayawada, Affliated to JNTU- Kakinada, A.P., India REFFERENCES AltovaXMLSpy : StylusStudio : ml Isparql: RDB2RDF: (Feb. 2010) SPARQLExtensions: ISSN: Page 2809

Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 1

Published by: PIONEER RESEARCH & DEVELOPMENT GROUP (www.prdg.org) 1 An Efficient Data Retrieval Using MashQL in the Data Web K.Saranya 1, T.Amruthavalli 2 1, 2 Department of Computer Science and Engineering, S.K.R Engineering College, Poonamallee, Tamil Nadu, India Abstract