ISSN Vol.05,Issue.07, July-2017, Pages:

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1 ISSN Vol.05,Issue.07, July-2017, Pages: Efficient Prediction of Difficult Keyword Queries over Databases KYAMA MAHESH 1, DEEPTHI JANAGAMA 2, N. ANJANEYULU 3 1 PG Scholar, Dept of CSE, Sphoorthy Engineering College, Hyderabad, TS, India, kshmahesh@gmail.com. 2 Associate Professor, Dept of CSE, Sphoorthy Engineering College, Hyderabad, TS, India, deepthi.janagama@gmail.com. 3 Assistant Professor, Dept of CSE, Sphoorthy Engineering College, Hyderabad, TS, India, anji.nerodi@gmail.com. Abstract: Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. Our empirical results show that our model predicts the hard queries with high accuracy. Further, we present a suite of optimizations to minimize the incurred time overhead. Keywords: Query Performance, Robustness, Query Effectiveness, Databases. I. INTRODUCTION Question interfaces (KQIs) for databases have attracted a IoT of attention within the last decade because of their flexibility and easy use in looking and exploring the data. Since any entity in an exceedingly information set that contains the question keywords may be a potential answer, keyword queries typically have several potential answers. KQIs should determine the information desires behind keyword queries and rank the answers so the required answers seem at the highest of the list. Unless otherwise noted, we tend to ask keyword query as question within the remainder of this paper. Some of the difficulties of answering a query are as follows: First, unlike queries in languages like SQL, users do not normally specify the desired schema element(s) for each query term. For instance, query Q1: Godfather on the IMDB database ( does not specify if the user is interested in movies whose title is Godfather or movies distributed by the Godfather Company. Thus, a KQI must find the desired attributes associated with each term in the query. Second, the schema of the output is not specified, i.e., users do not give enough information to single out exactly their desired entities. For example, Q1 may return movies or actors or producers. We present a more complete analysis of the sources of difficulty and ambiguity there are cooperative efforts to produce standard benchmarks and analysis platforms for keyword search strategies over databases. One effort is that the data-centric track of INEX Workshop wherever KQIs square measure evaluated over the well-known IMDB information set that contains structured info regarding movies and other people in show business. Queries were provided by participants of the workshop. Another effort is that the series of linguistics Search Challenges (SemSearch) at linguistics Search Workshop, where {the information the info the information} set is that the Billion Triple Challenge data set at 25.deri.de. It s extracted from completely different structured data sources over the online like Wikipedia. The queries square measure taken from Yahoo! keyword question log. Users have provided relevancy judgments for each benchmark. These results indicate that even with structured information, finding the specified answers to keyword queries remains a tough task. additional apparently, looking nearer to the ranking quality of the most effective playacting methods on each workshops, we tend to notice that all of them have been playacting terribly poorly on a set of queries. For instance, take into account the question ancient Rome era over the IMDB data set. Users would really like to check data regarding movies that state ancient Rome. For this question, the state-of-the- art XML search ways that we tend to enforced come rankings of significantly lower quality than their average ranking quality over all queries. Hence, some queries area unit more difficult than others. Moreover, regardless of that ranking method is employed; we tend to cannot deliver an inexpensive ranking for these queries. Such a trend has been additionally observed for keyword queries over text document collections. It is necessary for a KQI to acknowledge such queries and warn the user or use various techniques like question reformulation or question suggestions. It s going to additionally use techniques like question results diversification. To the most effective of our data, there has not been any work on predicting or analyzing the difficulties of queries over databases. Researchers have projected some ways to sight tough queries over plain text document collections. However, these techniques aren't applicable to our drawback since they ignore the structure of the information above all, as mentioned earlier, a KQI should assign every question term to 2017 IJIT. All rights reserved.

2 a schema element(s) within the information. It should additionally distinguish the specified result type(s). We tend to through empirical observation show that directs diversifications of these techniques area unit ineffective for structured data. II. EXISTING AND PROPOSED SYSTEMS A. Existing System There have been collaborative efforts to provide standard benchmarks and evaluation platforms for keyword search methods over databases. One effort is the data-centric track of INEX Workshop Queries was provided by participants of the workshop. Another effort is the series of Semantic Search Challenges (SemSearch).The results indicate that even with structured data, finding the desired answers to keyword queries is still a hard task more interestingly, looking closer to the ranking quality of the best performing methods on both workshops. B. Proposed System We set forth a principled framework and proposed novel algorithms to measure the degree of the difficulty of a query over a DB, using the ranking robustness principle. Based on our framework, we propose novel algorithms that efficiently predict the effectiveness of a keyword query. Advantages of Proposed System: 1. Easily mapped to both XML and relational data. 2. Higher prediction accuracy and minimize the incurred time overhead. Fig 1. System Architecture. III. LITERATURE SURVEY 1. Efficient IRstyle keyword search over relational databases, AUTHORS: V. Hristidis, L. Gravano, and Y. Papakonstantinou, Applications in which plain text coexists with structured data are pervasive. Commercial relational database management systems (RDBMSs) generally provide querying capabilities for text attributes that incorporate state-of-the-art information retrieval (IR) relevance ranking strategies, but this search functionality requires that queries specify the exact column or columns against which a given list of keywords is to be matched. This requirement can be cumbersome and inflexible from KYAMA MAHESH, DEEPTHI JANAGAMA, N. ANJANEYULU a user perspective: good answers to a keyword query might need to be "assembled" -in perhaps unforeseen ways- by joining tuples from multiple relations. This observation has motivated recent research on free-form keyword search over RDBMSs. In this paper, we adapt IR-style document-relevance ranking strategies to the problem of processing free-form keyword queries over RDBMSs. Our query model can handle queries with both AND and OR semantics, and exploits the sophisticated single-column text-search functionality often available in commercial RDBMSs. We develop query-processing strategies that build on a crucial characteristic of IR-style keyword search: only the few most relevant matches - according to some definition of "relevance"- are generally of interest.consequently, rather than computing all matches for a keyword query, which leads to inefficient executions, our techniques focus on the top-k matches for the query, for moderate values of k. A thorough experimental evaluation over real data shows the performance advantages of our approach. 2. SPARK: Top-k keyword query in relational databases, AUTHORS: Y. Luo, X. Lin, W. Wang, and X. Zhou, With the increasing amount of text data stored in relational databases, there is a demand for RDBMS to support keyword queries over text data. As a search result is often assembled from multiple relational tables, traditional IR-style ranking and query evaluation methods cannot be applied directly. In this paper, we study the effectiveness and the efficiency issues of answering top-k keyword query in relational database systems. We propose a new ranking formula by adapting existing IR techniques based on a natural notion of virtual document. Compared with previous approaches, our new ranking method is simple yet effective, and agrees with human perceptions. We also study efficient query processing methods for the new ranking method, and propose algorithms that have minimal accesses to the database. We have conducted extensive experiments on large-scale real databases using two popular RDBMSs. The experimentalresults demonstrate significant improvement to the alternative approaches in terms of retrieval effectiveness and efficiency. 3. A framework to improve keyword search over entity databases, AUTHORS: V. Ganti, Y. He, and D. Xin, Keyword search over entity databases (e.g., product, movie databases) is an important problem. Current techniques for keyword search on databases may often return incomplete and imprecise results. On the one hand, they either require that relevant entities contain all (or most) of the query keywords, or that relevant entities and the query keywords occur together in several documents from a known collection. Neither of these requirements may be satisfied for a number of user queries. Hence results for such queries are likely to be incomplete in that highly relevant entities may not be returned. On the other hand, although some returned entities contain all (or most) of the query keywords, the intention of the keywords in the query could be different from that in the entities. Therefore, the results could also be imprecise.

3 Efficient Prediction of Difficult Keyword Queries over Databases To remedy this problem, in this paper, we propose a general framework that can improve an existing search interface by translating a keyword query to a structured query. 4. A probabilistic retrieval model for semi-structured data, AUTHORS: J. Kim, X. Xue, and B. Croft, Retrieving semi-structured (XML) data typically requires either a structured query such as X-Path, or a keyword query that does not take structure into account. In this paper, we infer structural information automatically from keyword queries and incorporate this into a retrieval model. More specifically, we propose the concept of a mapping probability, which maps each query word into a related field (or XML element). This mapping probability is used as a weight to combine the language models estimated from each field. Experiments on two test collections show that our retrieval model based on mapping probabilities outperforms baseline techniques significantly. Fig Structured annotations of web queries, AUTHORS: N. Sarkas, S. Paparizos, and P. Tsaparas, Queries asked on web search engines often target structured data, such as commercial products, movie show times, or airline schedules. However, surfacing relevant results from such data is a highly challenging problem, due to the unstructured language of the web queries, and the imposing scalability and speed requirements of web search. In this paper, we discover latent structured semantics in web queries and produce Structured Annotations for them. We consider an annotation as a mapping of a query to a table of structured data and attributes of this table. Given a collection of structured tables, we present a fast and scalable tagging mechanism for obtaining all possible annotations of a query over these tables techniques are completely unsupervised, obviating the need for costly manual labeling effort. We evaluated our techniques using real world queries and data and present promising experimental results. Fig 4. IV. SCREEN SCREENS Fig 2. Fig.5

4 KYAMA MAHESH, DEEPTHI JANAGAMA, N. ANJANEYULU Fig.6 Fig.7 Fig.8 Fig.9 V. CONCLUSION We introduced the novel problem of predicting the effectiveness of keyword queries over DBs. We showed that the current prediction methods for queries over unstructured data sources cannot be effectively used to solve this problem. We set forth a principled framework and proposed novel algorithms to measure the degree of the difficulty of a query over a DB, using the ranking robustness principle. Based on our framework, we propose novel algorithms that efficiently predict the effectiveness of a keyword query. Our extensive experiments show that the algorithms predict the difficulty of a query with relatively low errors and negligible time overheads. VI. REFERENCES [1] V. Hristidis, L. Gravano, and Y. Papakonstantinou, Efficient IRstyle keyword search over relational databases, in Proc. 29 th VLDB Conf., Berlin, Germany, 2003, pp [2] Y. Luo, X. Lin, W. Wang, and X. Zhou, SPARK: Top-k keyword query in relational databases, in Proc ACM SIGMOD, Beijing, China, pp [3] V. Ganti, Y. He, and D. Xin, Keyword++: A framework to improve keyword search over entity databases, in Proc. VLDB Endowment, Singapore, Sept. 2010, vol. 3, no. 1 2, pp [4] J. Kim, X. Xue, and B. Croft, A probabilistic retrieval model for semistructured data, in Proc. ECIR, Tolouse, France, 2009, pp [5] N. Sarkas, S. Paparizos, and P. Tsaparas, Structured annotations of web queries, in Proc ACM SIGMOD Int. Conf. Manage. Data, Indianapolis, IN, USA, pp [6] G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan, Keyword searching and browsing in databases using BANKS, in Proc. 18th ICDE, San Jose, CA, USA, 2002, pp [7] C. Manning, P. Raghavan, and H. Schütze, An Introduction to Information Retrieval. New York, NY: Cambridge University Press, 2008.

5 Efficient Prediction of Difficult Keyword Queries over Databases [8] A. Trotman and Q. Wang, Overview of the INEX 2010 data centric track, in 9th Int. Workshop INEX 2010, Vugh, The Netherlands, pp. 1 32, [9]T. Tran, P. Mika, H. Wang, and M. Grobelnik, Semsearch S10, in Proc. 3rd Int. WWW Conf., Raleigh, NC, USA, [10] S. C. Townsend, Y. Zhou, and B. Croft, Predicting query performance, in Proc. SIGIR 02, Tampere, Finland, pp [11] A. Nandi and H. V. Jagadish, Assisted querying using instant response interfaces, in Proc. SIGMOD 07, Beijing, China, pp [12] E. Demidova, P. Fankhauser, X. Zhou, and W. Nejdl, DivQ: Diversification for keyword search over structured databases, in Proc. SIGIR 10, Geneva, Switzerland, pp Author's Profile: Kyama Mahesh Pursuing CSE from Sphoorthy Engineering College, Hyderabad, India. Deepthi Janagama has done Master of Science in Texas A&M University Commerce, Texas, USA and received the Bachelor Of Technology degree from Kamala Institute Of Technology And Science. She is currently working as Associate Professor and Head of the Department of CSE with Sphoorthy Engineering College, Nadergul. Her interested subjects are Database management system, Data mining and Data Warehousing, Digital Logic Design, Computer Programming, deepthi.janagama@gmail.com. N. Anjaneyulu, received the B.Tech (IT) from CVR College of Engineering, Hyderabad, and M.Tech(IT) from Guru Nanak Engineering College, Hyderabad, India in He is currently working as Assistant Professor in Sphoorthy Engineering College, Hyderabad, India. His area of interest includes Computer Networks, Network Security, Mobile Computing and Adhoc Sensor Networks. anji.nerodi@gmail.com