An algebraic theory of functional and multivalued dependencies in relational databases

Computation of the dependency basis is the fundamental step in solving the membership problem for functional dependencies (FDs) and multivalued dependencies (MVDs) in relational database theory. We examine this problem from an algebraic perspective. We introduce the notion of the inference basis of a set M of MVDs and show that it contains the maximum information about the logical consequences of M. We propose the notion of a dependency-lattice and develop an algebraic characterization of inference basis using simple notions from lattice theory. We also establish several interesting properties of dependency-lattices related to the implication problem. Founded on our characterization, we synthesize efficient algorithms for (a): computing the inference basis of a given set M of MVDs; (b): computing the dependency basis of a given attribute set w.r.t. M; and (c): solving the membership problem for MVDs. We also show that our results naturally extend to incorporate FDs also in a way that enables the solution of the membership problem for both FDs and MVDs put together. We finally show that our algorithms are more efficient than existing ones, when used to solve what we term the ‘generalized membership problem’.

Jatropha cultivation in southern India: assessing farmers' experiences

Together with 106 farmers who started growing Jatropha (Jatropha curcas L.) in 20042006, this research sought to increase the knowledge around the real-life experience of Jatropha farming in the southern India states of Tamil Nadu and Andhra Pradesh. Launched as an alternative for diesel in India, Jatropha has been promoted as a non-edible plant that could grow on poor soils, yield oil-rich seeds for production of bio-diesel, and not compete directly with food production. Through interviews with the farmers, information was gathered regarding their socio-economic situation, the implementation and performance of their Jatropha plantations, and their reasons for continuing or discontinuing Jatropha cultivation. Results reveal that 82% of the farmers had substituted former cropland for their Jatropha cultivation. By 2010, 85% (n = 90) of the farmers who cultivated Jatropha in 2004 had stopped. Cultivating the crop did not give the economic returns the farmers anticipated, mainly due to a lack of information about the crop and its maintenance during cultivation and due to water scarcity. A majority of the farmers irrigated and applied fertilizer, and even pesticides. Many problems experienced by the farmers were due to limited knowledge about cultivating Jatropha caused by poor planning and implementation of the national Jatropha program. Extension services, subsidies, and other support were not provided as promised. The farmers who continued cultivation had means of income other than Jatropha and held hopes of a future Jatropha market. The lack of market structures, such as purchase agreements and buyers, as well as a low retail price for the seeds, were frequently stated as barriers to Jatropha cultivation. For Jatropha biodiesel to perform well, efforts are needed to improve yield levels and stability through genetic improvements and drought tolerance, as well as agriculture extension services to support adoption of the crop. Government programs will -probably be more effective if implementing biodiesel production is conjoined with stimulating the demand for Jatropha biodiesel. To avoid food-biofuel competition, additional measures may be needed such as land-use restrictions for Jatropha producers and taxes on biofuels or biofuel feedstocks to improve the competitiveness of the food sector compared to the bioenergy sector. (c) 2012 Society of Chemical Industry and John Wiley & Sons, Ltd

Experiences With WM: A Centralised Scheduling Approach for Performance Management of IEEE 802.11 Wireless LANs

In our earlier work ([1]) we proposed WLAN Manager (or WM) a centralised controller for QoS management of infrastructure WLANs based on the IEEE 802.11 DCF standards. The WM approach is based on queueing and scheduling packets in a device that sits between all traffic flowing between the APs and the wireline LAN, requires no changes to the AP or the STAs, and can be viewed as implementing a "Split-MAC" architecture. The objectives of WM were to manage various TCP performance related issues (such as the throughput "anomaly" when STAs associate with an AP with mixed PHY rates, and upload-download unfairness induced by finite AP buffers), and also to serve as the controller for VoIP admission control and handovers, and for other QoS management measures. In this paper we report our experiences in implementing the proposals in [1]: the insights gained, new control techniques developed, and the effectiveness of the WM approach in managing TCP performance in an infrastructure WLAN. We report results from a hybrid experiment where a physical WM manages actual TCP controlled packet flows between a server and clients, with the WLAN being simulated, and also from a small physical testbed with an actual AP.

On Pushing Multilingual Query Operators into Relational Engines

To effectively support today’s global economy, database systems need to manage data in multiple languages simultaneously. While current database systems do support the storage and management of multilingual data, they are not capable of querying across different natural languages. To address this lacuna, we have recently proposed two cross-lingual functionalities, LexEQUAL[13] and SemEQUAL[14], for matching multilingual names and concepts, respectively. In this paper, we investigate the native implementation of these multilingual functionalities as first-class operators on relational engines. Specifically, we propose a new multilingual storage datatype, and an associated algebra of the multilingual operators on this datatype. These components have been successfully implemented in the PostgreSQL database system, including integration of the algebra with the query optimizer and inclusion of a metric index in the access layer. Our experiments demonstrate that the performance of the native implementation is up to two orders-of-magnitude faster than the corresponding outsidethe- server implementation. Further, these multilingual additions do not adversely impact the existing functionality and performance. To the best of our knowledge, our prototype represents the first practical implementation of a crosslingual database query engine.

Combining heterogeneous classifiers for relational databases

Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets. namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy. (C) 2012 Elsevier Ltd. All rights reserved.