A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Multi-way Theta-Join Based on CMD Storage Method

In the era of the Big Data, how to analyze such a vast quantity of data is a challenging problem, and conducting a multi-way theta-join query is one of the most time consuming operations. MapReduce has been mentioned most in the massive data processing area and some join algorithms based on it have been raised in recent years. However, MapReduce paradigm itself may not be suitable to some scenarios and multi-way theta-join seems to be one of them. Many multi- way theta-join algorithms on traditional parallel database have been raised for many years, but no algorithm has been mentioned on the CMD (coordinate modulo distribution) storage method, although some algorithms on equal-join have been proposed. In this paper, we proposed a multi-way theta-join method based on CMD, which takes the advantage of the CMD storage method. Experiments suggest that it's a valid and efficient method which achieves significant improvement compared to those applied on the MapReduce

Improving Temporal Joins Using Histograms

Histograms are used in most commercial database systems to estimate query result sizes and evaluation plan costs. They can also be used to optimize join algorithms. In this paper we consider how to use histograms to improve the join processing in temporal databases. We dene histograms for temporal data and a temporal join algorithm that makes use of this histogram information. The join algorithm is a temporal partition-join with dynamic buer allocation. Histogram information is used to determine partition boundaries that maximize overall buer usage. We compare the performance of this join algorithm to temporal join evaluation strategies that do not use histograms, such as a partitionbased algorithm based on sampling and a partition-join using the Time Index, an index structure for temporal data. The results demonstrate that the temporal partition-join is substantially improved through the incorporation of histogram information, showing signicantly better performance ..

KNR-tree: A novel R-tree-based index for facilitating Spatial Window Queries on any k relations among N spatial relations in Mobile environments

10.1145/1071246.1071272Proceedings - Sixth International Conference on Mobile Data Management, MDM'05173-17

Dynamic multi-resolution spatial object derivation for mobile and WWW applications

Online geographic information systems provide the means to extract a subset of desired spatial information from a larger remote repository. Data retrieved representing real-world geographic phenomena are then manipulated to suit the specific needs of an end-user. Often this extraction requires the derivation of representations of objects specific to a particular resolution or scale from a single original stored version. Currently standard spatial data handling techniques cannot support the multi-resolution representation of such features in a database. In this paper a methodology to store and retrieve versions of spatial objects at, different resolutions with respect to scale using standard database primitives and SQL is presented. The technique involves heavy fragmentation of spatial features that allows dynamic simplification into scale-specific object representations customised to the display resolution of the end-user's device. Experimental results comparing the new approach to traditional R-Tree indexing and external object simplification reveal the former performs notably better for mobile and WWW applications where client-side resources are limited and retrieved data loads are kept relatively small