Optimal Enhancement of Location Aware Spatial Keyword Cover

Transcription

1 Optimal Enhancement of Location Aware Spatial Keyword Cover Simran Lalwani, Tanmay Dixit, Ghandhrva Malhotra, Abhimanyu, Shrikala Deshmukh, Snehal Chaudhary, Priyanka Paygude Abstract Spatial Database more the uncommon word more is it associated to the life of every individual in several different ways. These spatial databases have objects that have certain keywords associated to represent their businesses, services provided or any specific features. Closest Keywords Search an associated problem is to process objects known as keyword cover, that together surrounds a set of query keywords that have a minimum inter object distance and also are closer to the search location. Keyword Rating can affect various decision making situations that evaluate objects. Best Keyword Cover the most common version of Closest Keywords Search takes into consideration the minimum inter object distance and the respective keyword ratings. The Baseline Algorithm tends to combine objects from different query keywords to exhibit candidate keyword cover. But the baseline algorithm fails to exhibit its performance level when query keywords increase in number. To overcome this drawback we use more scalable algorithm called keyword nearest neighbour expansion (keyword-nne) that comparatively is more superior and shows a significant decrease in the number of candidate covers generated. To optimize the solution even more we use the distance of the query objects from the current location. Optimal Enhancement involves considering not only the user s current location but also the actual travel path and time rather than the Euclidean distance. Index Terms Keyword search, k-nne, Spatial database, user-ratings. I. INTRODUCTION Today location based services occupy a major section of the applications that are driven by mobile computing. What acts as the iron in the fire is the easy and open availability of digital maps and satellite imagery. The above mentioned are the major reasons that lead to the immense attention gained by spatial keyword search. A. Spatial Database Spatial database is an optimized collection of geometric objects i.e. lines, points and polygons. It also stores and processes data that represent objects in a given geometric space. Each tuple in these databases represents an object that has associated keywords to represent its business, service or feature. When we come across a set of query keywords, an elementary feature of spatial keywords search is to recognize spatial objects that are associated with keywords relevant to a set of query keywords, and also have required relationships such as close to each other, close to a user s given location or better rating. B. Keyword Search The above given problem finds its importance in several applications as the users desirables are often given as multiple keywords. The modern applications demand objects to be categorized based on both their geometric coordination and text associated to them. For example the application would be extremely helpful if it could search the nearest hotel that also offers a number of different facilities such as swimming pool, restaurant, Wi-Fi, cab services, etc. When the user enters multiple keywords it expects to retrieve results that fulfil all needs without long distance travelling. C. mck Query Practically there will be no individual objects are related to the entire set of given query keywords, this triggers the obvious need to retrieve multiple objects, known as keyword cover. It covers all the query keyword together with minimum inter-distance among the objects. This problem can be referred to as m Closest Keywords (mck) query. D. Best Keyword Cover The more generalized version of mck Query known as the Best Keyword Cover (BKC) Query considers not only the inter-object distance but also the keyword rating. According to a survey that Dimensional Research conducted in 2013, 90 percents of the people claim that buying decisions are based on the online business rating. BKC query supports more vigorous evaluation of the objects thus leading to better decisions. There are two BKC query processing algorithms namely baseline and keyword-nne. The baseline and keyword-nne algorithms are supported by indexing the objects with an R*-tree like index called KRR* tree. E. Baseline Algorithm The trigger is to combine nodes in higher hierarchical levels of KRR*-trees to generate candidate keyword covers. Proceeding this the most promising candidate is assessed in priority by combining their child nodes to generate new candidates. It is easy to resolve a BKC Query but the problem 73

2 arises when the number of query keywords increases, this causes the performance to drop drastically as a result of massive candidate keyword covers generated. F. Keyword NNE Algorithm To overcome the drawback with the baseline algorithm we come apply a different strategy termed as k-nne. The algorithm considers one keyword as the principal one and objects associated with the principal keyword are termed as principal objects. This approach is more scalable. The local best solution called local best keyword cover (lbkc) is computed for every principal objects. Finally, the lbkc with the maximum evaluation is the solution of BKC query. LBKC of a given principal object will be known by merely getting back few highly rated objects from each query keyword which are non principal. The number of candidate keywords that are in NNE algorithm need to be reduced. The deep analysis shows that k-nne algorithm has processed number of candidate keyword cover and each processing generates lesser new candidates keyword cover. II. RELATED WORK Spatial database contains the information about the spatial objects which are associated with the keywords to indicate the information such as its business/services/features. Individual objects are not associated with every query keywords this help us to study to get back multiple objects that together cover all query keywords and which are close to each other. In m closest keyword search, it encompasses query keywords and minimum distance between objects. From last few years, availability of keyword rating has increased as well as its importance in object evaluation for the decision making. That is why the new algorithm called best keyword cover is developed which considers inter-distance as well as the keyword rating mentioned in online business that are provided by the customer. m closest keyword search algorithm generates candidate keyword covers by combining objects from different query keywords. Baseline algorithm and keyword nearest neighbor expansion algorithms are used to find the best keyword cover. When the query keywords are increased it affects the performance of the closest keyword algorithm. This work proposes to solve generic version problem of the existing algorithm called keyword nearest neighbor expansion which reduces the resulted candidate key. Author[1] proposes that the keyword rating and inter-object distance of objects are the output generated by bkc query. On the basis of the personal experience of the user, keyword rating of the objects is provided that are very much important in decision making. Author[2] proposes that Keyword Cover query provides a new improved spatial keyword searching method, in which keyword rating is considered. The method is based on keyword- NNE algorithm. In location aware closest keyword search in spatial data user s current location is also taken. So the method is based on location aware closest spatial keyword querying. Along with the results of keyword-nne, users current location and distance of the path is retrieved. Author in [3] proposes that various applications need to find objects closest to the mentioned location that has a set of keywords. In a spatial dataset, objects are linked with some keyword(s) that specify their features. Closest Keywords is a method of using keyword cover for objects. Algorithm which is based on Closest Keywords Search that exhaustively combines objects from different query keywords for discovering candidate keyword covers. The better decision depends on the evaluation of object keyword rating. Author[4] proposes that, we have studied the problem of enhancing the visibility of database objects through exploratory search and exploratory analysis of preference queries. It describes an algorithm that instead of presenting to the users a list of results, they organize search results into groups, that help users to have a wide overview of the data content related to their query. It also proposes algorithms for exploratory analysis of preference queries that reveal features that have a potential of being attractive to a wide range of users. This information can be used by companies or organizations that wish to attract new users and make their products or data more visible to their user-base. Author[5] address the increasing influence of spatio-textual objects as a challenging situation by making better description of textual and selected keywords. Due to this spatio-textual objects resembling to user queries are increased, with the important goal that is making object a part of top k result that is different for every users. We provide statement that comprises of top k and reverse top queries concepts. We present solution for the NP hard problems. Author[6] address the different methods and techniques used for searching spatial database based on nearest neighbor search. Many problems were encountered in pervious methods. The solutions were also way too expensive for space consumption. So to overcome these problems encountered in previous methods we make use of a new method that reduces the search space and it also increases the efficiency of nearest neighbor search. III. BACKGROUND ON SPATIAL KEYWORD SEARCH Consider a spatial database that has objects which are associated with multiple keywords simultaneously. Such objects are separated on the basis of the keywords and converted into a form such that each object in the spatial database is in the form, where x and y define the location of the object in the 2-D geometric space. A. Diameter Let O be a set of objects in the spatial database {o1,,on}. For oi,oj O, dist(oi,oj) is the Euclidean distance between All Rights Reserved 2016 IJARCSEE 74

3 oi,oj in the 2-dimensional geographical space. The diameter of O is: Fig. O.score Calculation (a) The linear interpolation function is used to obtain the score of O such that the score is a linear interpolation of the individually normalized diameter and the minimum keyword rating of O. The score of O function not only considers the maximum distance but also the keyword rating of objects. B. Keyword Cover Let T be a set of keywords {k1,,kn} and Z a set of objects {z1,,zn},z is a keyword cover of T if one and only one keyword in T are associated with one object in Z. C. Best Keyword Cover Consider D, a spatial database and T, a set of query keywords Best Keyword Cover query returns a keyword cover Z of T (Z D) such that Z.score Z 0.score for any keyword cover Z0 of T (Z0 D). Given a set of objects Zi, suppose Zj is a subset of Zi. The diameter of Zi must be not less than that of Zj, and the minimum keyword rating of objects in Zi must be not greater than that of objects in Zj. Therefore, Zi.score Zj.score. D. Indexing Keywords Rating Augmenting one additional dimension with R*-tree to include keyword ratings. There are different measures of spatial and keyword rating dimension. Dimensions are bought back to its original state for both spatial and keyword rating ranges. Suppose we need to construct a KRR*-tree over a set of objects D. Each object o D is mapped into a new space using the following mapping function: IV. PROPOSED METHOD A. Baseline Algorithm Learn to create predictions for a dataset. You can use the predictions to measure the baseline's performance (e.g., accuracy) the metric will then become what you compare it with any other machine learning algorithm. A machine learning algorithm tries to learn a function that build the relationship between the A baseline is a method that make use of simple summary statistics, randomness, or machine input (feature) data and the target variable (or label). When you test it, you will typically measure performance. For example, your algorithm may be 75% accurate. You can infer this meaning by comparing with a baseline's performance. In general, you will want your approach to outperform the baselines you have selected. In the example above, you want your 75% accuracy to be higher than any baseline you will run on the same data. Baseline algorithm focuses on retrieving data with respect to the given query keywords. Best keyword cover can be obtained by baseline algorithm. It requires spatial object in file format that includes location and its document identifier and its address. Input in baseline algorithm requires single query keyword in the form of objects. The first step is to set a variable bkc as zero. Next step is to generate candidate keyword cover, candidate keyword cover generate spatial objects that contain those query keyword. Now frequency inverse is use to calculate keyword significance, frequency inverse is a combination of term frequency and inverse document frequency. The default value is set as zero. The score we get is compared with first score. If the value is greater than zero, then set it as best keyword cover. Score calclation can be obtained as ap-running strategy. After all this, next step is to perform nearest neighbour search on candidate keyword covers generated. Fig. O.score Calculation (b) In the above equation maxx, maxy, objects in D has maximum max_rating on the dimensions of x,y keyword rating.construction of KKR* tree and 3 dimensional R*TREE are same. KKR* trees every nodes are defined as N(x,y,r,lx,ly,lr) where x is the value of N in x axle close to the origin, i.e., (0,0,0,0,0,0), and lx is the width of N in x axle, so do y, ly and r, lr. Fig. Baseline Algorithm 75

4 B. Keyword nearest neighbor expansion variant algorithm Problem with baseline algorithm is that it can t respond to multiple query keywords. It does not respond to spatial features. To overcome this we make use of nearest neighbour algorithm called nearest neighbour expansion variant. This algorithm focuses both on keyword search and nearest neighbour search. It introduces concept of keyword rating, spatial relevance and keyword relevance. Keyword rating helps in decision making and plays a significant role. As the performance depends on objects, so their exist a problem of choosing which objects first for querying when given multiple features of different objects. So for this purpose we make use of keyword rating. Rating of an object is based on its daily life. Rating values ranges between 1 to 5. The first step is to select principle query keyword to perform search. Objects linked to principle query are called principle objects. After identifying the objects it searches for the object with higher keyword rating.the one with highest keyword rating are set as first object in which search has to be carried out. It also helps to find best route search. If feature is set as input, the first step is to identify the object with highest keyword rating. After that nearest neighbour search algorithm is performed to find nearest neighbour of user s query with respect to current location. description to the query keywords and distance to the query location (a point). Range Query: Objects whose text description contains user specified keywords and location is within a range specified by the user are retrieved. Object set oriented is based on retrieval of group of different types of objects. m Closest Keyword (mck), nearest neighbor search and keyword cover search and collective spatial keyword query are included under this category. It includes retrieval of multiple types of objects satisfying certain query keywords: m Closest Keyword query: Retrieves m closest objects which can cover all the query keywords Collective spatial keyword query: Retrieves collection of objects in a particular location. User s location and keywords are given as input parameters. V. LOCATION AWARE CLOSEST KEYWORD SEARCH IN SPATIAL DATA The method is based on current location of user. User specifies his points of interest and current location. After calculating GBKC, the system returns an itinerary (a planned route) covering user s current location and POIs (Points of Interest). Initially specify the current location of the user. Using Geo-coding API, corresponding address is converted to its latitude and longitude. From the current location nearest object in GBKC is calculated, and the process continues upto the last object. Fig. Keyword-NNE Algorithm C. Querying Methods Mainly there are two types of spatial keyword querying: Single object oriented Object set oriented Single object oriented queries are based on the retrieval of a single type of object. User give a single type of object and the retrieved result are based on user s location or given descriptions about keyword. Single object oriented includes following types of queries: knn Query: Retrieve the k objects nearest to the users current location (represented by a point). Top-k nearest neighbor Query: Top k objects with the highest ranking scores are retrieved, measured as a combination text A. Euclidean Distance versus Travelling Distance Spatial distance is an inevitable component of theories across the social, natural, and information sciences. Most of the spatial database applications are based on Euclidean distance. It is the straight line distance between two spatial points and which provides the shortest distance. But in real life applications user s interest is to get the travelling distance. In-stead of calculating straight line (Euclidean) distance calculate travelling distance. Travelling distance will be more accurate than Euclidean distance. B. Google API We are using Google API for location. Google APIs is a group of application programming inter-faces (APIs) which is developed by Google that allows communication with Google Services along integration to other services. Thirdparty applications can utilize the APIs to take advantage of functionality of the existing services. Here in this work various Google map APIs are used to calculate distance, duration and direction of the path. Following Google APIs are utilized here. All Rights Reserved 2016 IJARCSEE 76

5 VI. SYSTEM ARCHITECTURE Conference on Futuristic Trends in Research and Innovation for Social Welfare (WCFTR 16), [4] Orestis Gkorgkas, Database Content Exploration and Exploratory Analysis of User Queries, Doctoral theses at NTNU, [5] O. Gkorgkas, A. Vlachou, C. Doulkeridis and K. Nørvåg, Maximizing Influence of Spatio-Textual Objects Based on Keyword Selection, in Advances in Spatial and Temporal Databases, Volume 9239 of the series Lecture Notes in Computer Science, pp [6] Z.Anjum and P.Saktel, Nearest Neighbor Search Technique for Spatial Database, International Journal of Computer Science and Information Technologies, Vol. 5 (6), 2014, [7] D.W. Choi, J. Pei and X. Lin, Finding the Minimum Spatial Keyword Cover, in Data Engineering Conference(ICDE), 2016 IEEE. [8] A. B. Birla and P. Kaur, A Review of Best Keyword Cover Search, in Journal of Data Mining and Management, Volume 1, Issue, [9] C. Luoa, L. Junlinb, G. Lia and W. Wei, Efficient reverse spatial and textual k nearest neighbor queries on road networks, Knowledge-Based Systems, Volume93, 1 February 2016, Pages Simran Lalwani student at Department of Information Technology, Bharati Tanmay Dixit student at Department of Information Technology, Bharati Ghandhrva Malhotra student at Department of Information Technology, Bharati Abhimanyu student at Department of Information Technology, Bharati Fig. System Architecture VII. CONCLUSION AND FUTURE SCOPE Keyword Cover query provides a new improved spatial keyword searching method, in which keyword rating is considered. The method is based on keyword- NNE algorithm. In location aware closest keyword search in spatial data user s current location is also taken. So the method is based on location aware closest spatial keyword querying. Along with the results of keyword-nne, user s current location and distance of the path is retrieved. To get a path to users points of interest Google waypoints are utilized. Instead of Euclidean distance, travelling distance is calculated to get accurate results. So that an itinerary covering all the POIs is created and the traveler can make sensible decision to arrange a trip to his interested location. The scope of future work relies on the following cases. The default mode of transportation is driving; all other modes Shrikala Deshmukh completed her BE in IT from Shivaji University and MTech in IT from Bharati Vidyapeeth Deemed University Snehal Chaudhary completed her BE in IT from Godavari College of Engineering, Jalgaon, North Maharashtra University and MBA Systems Management at Godavari Institute of Management and Research and ME in Computer from PICT, Pune. Priyanka Paygude completed her BE in Computer from Pune University and Mtech in IT from Bharati Vidyapeeth University. REFERENCES [1] X.Li, J. Lu and X.Zhou, BEST KEYWORD COVER SEARCH, in Knowledge and Data Engineering, IEEE Transactions, Volume:27, Issue: 1, May [2] T.P. Hasana and S. Farook, An Enhanced Location Aware Closest Keyword Search in Spatial Data, in International Journal of Current Trends in Engineering & Research (IJCTER), Volume 2, Issue 4, April 2016, pp [3] P. Mahure, Better Performing Keywords Cover Search, in World 77