Volume Introduction Letter

This Conference Proceedings is a collection of outstanding papers submitted to the Academic Program of the International Conference for Free and Open Source Software for Geospatial (FOSS4G), 14th to 19th September 2015 in Seoul, South Korea

VAMANA : A High Performance, Scalable and Cost Driven XPath Engine

Many applications are migrating or beginning to make use native XML data. We anticipate that queries will emerge that emphasize the structural semantics of XML query languages like XPath and XQuery. This brings a need for an efficient query engine and database management system tailored for XML data similar to traditional relational engines. While mapping large XML documents into relational database systems while possible, poses difficulty in mapping XML queries to the less powerful relational query language SQL and creates a data model mismatch between relational tables and semi-structured XML data. Hence native solutions to efficiently store and query XML data are being developed recently. However, most of these systems thus far fail to demonstrate scalability with large document sizes, to provide robust support for the XPath query language nor to adequately address costing with respect to query optimization. In this thesis, we propose a novel cost-driven XPath engine to support the scalable evaluation of ad-hoc XPath expressions called VAMANA. VAMANA makes use of an efficient XML repository for storing and indexing large XML documents called the Multi-Axis Storage Structure (MASS) developed at WPI. VAMANA extensively uses indexes for query evaluation by considering index-only plans. To the best of our knowledge, it is the only XML query engine that supports an index plan approach for large XML documents. Our index-oriented query plans allow queries to be evaluated while reading only a fraction of the data, as all tuples for a particular context node are clustered together. The pipelined query framework minimizes the cost of handing intermediate data during query processing. Unlike other native solutions, VAMANA provides support for all 13 XPath axes. Our schema independent cost model provides dynamically calculated statistics that are then used for intelligent cost-based transformations, further improving performance. Our optimization strategy for increasing execution time performance is affirmed through our experimental studies on XMark benchmark data. VAMANA query execution is significantly faster than leading available XML query engines

Supporting Multi-Criteria Decision Support Queries over Disparate Data Sources

In the era of big data revolution, marked by an exponential growth of information, extracting value from data enables analysts and businesses to address challenging problems such as drug discovery, fraud detection, and earthquake predictions. Multi-Criteria Decision Support (MCDS) queries are at the core of big-data analytics resulting in several classes of MCDS queries such as OLAP, Top-K, Pareto-optimal, and nearest neighbor queries. The intuitive nature of specifying multi-dimensional preferences has made Pareto-optimal queries, also known as skyline queries, popular. Existing skyline algorithms however do not address several crucial issues such as performing skyline evaluation over disparate sources, progressively generating skyline results, or robustly handling workload with multiple skyline over join queries. In this dissertation we thoroughly investigate topics in the area of skyline-aware query evaluation. In this dissertation, we first propose a novel execution framework called SKIN that treats skyline over joins as first class citizens during query processing. This is in contrast to existing techniques that treat skylines as an add-on, loosely integrated with query processing by being placed on top of the query plan. SKIN is effective in exploiting the skyline characteristics of the tuples within individual data sources as well as across disparate sources. This enables SKIN to significantly reduce two primary costs, namely the cost of generating the join results and the cost of skyline comparisons to compute the final results. Second, we address the crucial business need to report results early; as soon as they are being generated so that users can formulate competitive decisions in near real-time. On top of SKIN, we built a progressive query evaluation framework ProgXe to transform the execution of queries involving skyline over joins to become non-blocking, i.e., to be progressively generating results early and often. By exploiting SKIN\u27s principle of processing query at multiple levels of abstraction, ProgXe is able to: (1) extract the output dependencies in the output spaces by analyzing both the input and output space, and (2) exploit this knowledge of abstract-level relationships to guarantee correctness of early output. Third, real-world applications handle query workloads with diverse Quality of Service (QoS) requirements also referred to as contracts. Time sensitive queries, such as fraud detection, require results to progressively output with minimal delay, while ad-hoc and reporting queries can tolerate delay. In this dissertation, by building on the principles of ProgXe we propose the Contract-Aware Query Execution (CAQE) framework to support the open problem of contract driven multi-query processing. CAQE employs an adaptive execution strategy to continuously monitor the run-time satisfaction of queries and aggressively take corrective steps whenever the contracts are not being met. Lastly, to elucidate the portability of the core principle of this dissertation, the reasoning and query processing at different levels of data abstraction, we apply them to solve an orthogonal research question to auto-generate recommendation queries that facilitate users in exploring a complex database system. User queries are often too strict or too broad requiring a frustrating trial-and-error refinement process to meet the desired result cardinality while preserving original query semantics. Based on the principles of SKIN, we propose CAPRI to automatically generate refined queries that: (1) attain the desired cardinality and (2) minimize changes to the original query intentions. In our comprehensive experimental study of each part of this dissertation, we demonstrate the superiority of the proposed strategies over state-of-the-art techniques in both efficiency, as well as resource consumption

Advances in Free Software Geographic Information Systems

ABSTRACT: This paper reports on recent advances in FOSS4G (Free and Open Source Software for Geoinformatics). Features of the new GRASS 6.2.0 release, a major software package among the FOSS4G product family, are summarised in this paper. The new capabilities of GRASS 6 include a new engine for spatial vector data processing as well as an improved user interface with internationalisation efforts and multi-byte font support. Spatial data are crucial for national and local development planning, disaster management and early warning, and environmental monitoring. Compared to other IT sectors

Progressive Result Generation for Multi-Criteria Decision Support Queries

Multi-criteria decision support (MCDS) is crucial in many business and web applications such as web searches, B2B portals and on-line commerce. Such MCDS applications need to report results early; as soon as they are being generated so that they can react and formulate competitive decisions in near real-time. The ease in expressing user preferences in web-based applications has made Pareto-optimal (skyline) queries a popular class of MCDS queries. However, state-of-the-art techniques either focus on handling skylines on single input sets (i.e., no joins) or do not tackle the challenge of producing progressive early output results. In this work, we propose a progressive query evaluation framework ProgXe that transforms the execution of queries involving skyline over joins to be non-blocking, i.e., to be progressively generating results early and often. In ProgXe the query processing (join, mapping and skyline) is conducted at multiple levels of abstraction, thereby exploiting the knowledge gained from both input as well as mapped output spaces. This knowledge enables us to identify and reason about abstractlevel relationships to guarantee correctness of early output. It also provides optimization opportunities previously missed by current techniques. To further optimize ProgXe, we incorporate an ordering technique that optimizes the rate at which results are reported by translating the optimization of tuple-level processing into a job-sequencing problem. Our experimental study over a wide variety of data sets demonstrates the superiority of our approach over state-of-the-art techniques