Efficient Processing of Top-k Frequent Spatial Keyword Queries

The rapid popularization of high-speed mobile communication technology and the continuous development of mobile network devices have given spatial textual big data (STBD) new dimensions due to their ability to record geographical objects from multiple sources and with complex attributes. Data mining from spatial textual datasets has become a meaningful study. As a popular topic for STBD, the top-k spatial keyword query has been developed in various forms to deal with different retrievals requirements. However, previous research focused mainly on indexing locational attributes and retrievals of few target attributes, and these correlations between large numbers of the textual attributes have not been fully studied and demonstrated. To further explore interrelated-knowledge in the textual attributes, this paper defines the top-k frequent spatial keyword query (tfSKQ) and proposes a novel hybrid index structure, named RCL-tree, based on the concept lattice theory. We also develop the tfSKQ algorithms to retrieve the most frequent and nearest spatial objects in STBD. One existing method and two baseline algorithms are implemented, and a series of experiments are carried out using real datasets to evaluate its performance. Results demonstrated the effectiveness and efficiency of the proposed RCL-tree in tfSKQ with the complex spatial multi keyword query conditions.

Time-aware collective spatial keyword query

The collective spatial keyword query is a hot research topic in the database community in recent years, which considers both the positional relevance to the query location and textual relevance to the query keywords. However, in real life, the temporal information of object is not always valid. Based on this, we define a new query, namely time-aware collective spatial keyword query (TCoSKQ), which considers the positional relevance, textual relevance, and temporal relevance between objects and query at the same time. Two evaluation functions are defined to meet different needs of users, for each of which we propose an algorithm. Effective pruning strategies are proposed to improve query efficiency based on the two algorithms. Finally, the experimental results show that the proposed algorithms are efficient and scalable.

Design and Analysis of Spatial Skyline Queries Indexing

Dwelling in the information age permits nearly everybody needs to recover countless information and choices to gather from to fulfill their necessities. In distinctive cases, the quantity of information accessible and the speed of change may cover the ideal and required explanation. Spatial-textual queries provide the most acclaimed nearest points concerning a conveyed site and a keyword set. Current practice regularly thought on the most capable technique to expertly get the top-k resultset reestablished a spatial-scholarly query. A capable Spatial Range Skyline Query (SRSQ) algorithm is proposed which initially performsa spatial keyword query (SKQ) that relies upon an IRtree that documents the information. Skyline centers picked are not simply established on their partitions to a lot of inquiries and more subject to their significance to a social occasion of query keywords. Additionally, besides proposed range skyline (RS) methods based on R-tree multi-dimensional space including secondary- memory pruning tools for operating field skyline queries is accomplished. The advanced scheme is dynamic and I/O optimum. Ultimately, methodology presents a modern assessment that demonstrates the proficiency.

Geo-Social Top-k and Skyline Keyword Queries on Road Networks

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.