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In this paper, we present HomeSeeker, an interactive visual analytics system to serve users with different backgrounds of the local real estate market and meet different degrees of user requirements. As a result, HomeSeeker augments existing commercial systems to help users discover hidden patterns, link various location-centered data to the price, as well as explore, filter and compare the properties, in order to easily find their preferred properties. In particular, we make the following contributions: (1) We present a problem abstraction for designing visualizations that help home buyers analyse the real estate data. Specifically, our data abstraction integrates heterogeneous data from different channels into a location-centred integrated real estate dataset. (2) We propose an interactive visual analytic procedure to help less informed users gradually learn about the local real estate market, upon which users exploit this learned knowledge to specify their individual requirements in property seeking. (3) We propose a series of designs to visualize properties/suburbs in different dimensions and in different granularities. We have collected, integrated and cleaned last 10 year’s real estate sold records in Australia as well as their location-related education, facility and transportation profiles, to generate a real multi-dimensional data repository, and implemented a system prototype for public access ( http://115.146.89.158 ). At last, we present case studies based on real-world datasets and real scenario to demonstrate the usefulness and effectiveness of our system. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the guest editors discuss articles in the issue on topics including passive and partially active (PPA) fault tolerance, smart drill-down for data exploration, and core graph decomposition.

Abstract: Optimizing queries using materialized views has not been addressed adequately in the context of XML due to the many limitations associated with the definition and usability of materialized views in traditional XML query evaluation models. In this paper, we address the XML query optimization problem using materialized views in the framework of the inverted lists evaluation model which has been established as the most prominent one for evaluating queries on large persistent XML data. Under this framework, we propose a novel approach which instead of materializing the answer of a view materializes exactly the sublists of the inverted lists that are necessary for computing the answer of the view. A further originality of our approach is that the view materializations are stored as compressed bitmaps. This technique not only minimizes the materialization space but also reduces CPU and I/O costs by translating view materialization processing into bitwise operations. Our approach departs from the traditional approach which identifies a compensating expression that rewrites the query using the materialized views. Instead, it computes the query answer by executing holistic stack-based algorithms on the view materializations. We experimentally compared our approach with recent outstanding structural summary and B-tree based approaches. In order to make the comparison more competitive we also proposed an extension of a structural index approach to resolve combinatorial explosion problems. Our experimental results show that our compressed bitmapped materialized views approach is the most efficient, robust, and stable one for optimizing XML queries. It obtains significant performance savings at a very small space overhead and has negligible optimization time even for a large number of materialized views in the view pool. [Copyright &y& Elsevier]

XML query languages typically allow the specification of structural patterns using XPath. Usually, these structural patterns are in the form of trees (Tree-Pattern Queries—TPQs). Finding the occurrences of such patterns in an XML tree is a key operation in XML query evaluation. The multiple previous algorithms presented for this operation focus mainly on the evaluation of tree-pattern queries. Recently, requirements for flexible querying of XML data have motivated the consideration of query classes that are more expressive and flexible than TPQs for which efficient nonmain-memory evaluation algorithms are not known. In this paper, we consider a class of queries, called Partial Tree-Pattern Queries (PTPQs), which generalize and strictly contain TPQs. PTPQs represent a broad fragment of XPath which is very useful in practice. In order to process PTPQs, we introduce a set of sound and complete inference rules to characterize structural relationship derivation. We provide necessary and sufficient conditions for detecting query unsatisfiability and node redundancy. We also show that PTPQs can be represented as directed acyclic graphs augmented with the “same-path” constraints. In order to leverage existing efficient evaluation algorithms for less expressive classes of queries, we design two approaches that evaluate a PTPQ by decomposing it into a set of simpler queries: algorithm IndexTPQGen, exploits a structural summary of the XML data and evaluates a PTPQ by generating an equivalent set of TPQs and unioning their answers. Algorithm PartialPathJoin decomposes the PTPQ into partial-path queries, and merge-joins their solutions. We also develop PartialTreeStack, an original polynomial time holistic algorithm for PTPQs. To the best of our knowledge, this is the first algorithm to support the evaluation of such a broad structural fragment of XPath in the inverted lists evaluation model. We provide a theoretical analysis of our algorithm and identify cases where it is asymptotically optimal. An extensive experimental evaluation shows that it is more efficient, robust, and stable than the other two and it outperforms a state-of-the art XQuery engine on PTPQs. [ABSTRACT FROM PUBLISHER]