Mining Multi-Relational Gradual Patterns

International audienceGradual patterns highlight covariations of attributes of the form " The more/less X, the more/less Y ". Their usefulness in several applications has recently stimulated the synthesis of several algorithms for their automated discovery from large datasets. However, existing techniques require all the interesting data to be in a single database relation or table. This paper extends the notion of gradual pattern to the case in which the co-variations are possibly expressed between attributes of different database relations. The interestingness measure for this class of " relational gradual patterns " is defined on the basis of both Kendall's τ and gradual supports. Moreover, this paper proposes two algorithms, named τ RGP Miner and gRGP Miner, for the discovery of relational gradual rules. Three pruning strategies to reduce the search space are proposed. The efficiency of the algorithms is empirically validated, and the usefulness of relational gradual patterns is proved on some real-world databases

Concept analysis-based association mining from linked data: A case in industrial decision making

International audienceLinked data (LD) is a rich format increasingly exploited in knowledge discovery from data (KDD). To that end, LD is typically structured as graph, but can also fit the multi-relational data mining (MRDM) paradigm, e.g. as multiple types and object properties may be used in the dataset. Formal concept analysis (FCA) has been successfully used as theoretical framework for KDD in a variety of applications , primely in clustering and association rule mining (ARM) tasks. As FCA applicability to LD is limited by its single data table input format, relational concept analysis (RCA) was introduced as a MRDM extension that successfully deals with links in the data, including cyclic ones. While RCA has been mainly adapted for conceptual clustering in the past, we present here an RCA-based ARM method. It exploits the iterative nature of pattern generation to cut cyclic references with a minimal loss of information. The utility of the rules discovered by our method has been validated by an application as a decision support in the aluminum die casting industry

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development

Flattening an object algebra to provide performance

Algebraic transformation and optimization techniques have been the method of choice in relational query execution, but applying them in object-oriented (OO) DBMSs is difficult due to the complexity of OO query languages. This paper demonstrates that the problem can be simplified by mapping an OO data model to the binary relational model implemented by Monet, a state-of-the-art database kernel. We present a generic mapping scheme to flatten data models and study the case of straightforward OO model. We show how flattening enabled us to implement a query algebra, using only a very limited set of simple operations. The required primitives and query execution strategies are discussed, and their performance is evaluated on the 1-GByte TPC-D (Transaction-processing Performance Council's Benchmark D), showing that our divide-and-conquer approach yields excellent result

The 2014 Australia-China trade report

Examines the benefits of the Australia-China trading relationship at a household level and looks beyond the resources boom and exploring the growth other Australian industries are seeing with China. It also provides practical advice on how to do business in China from Australian businesses already successfully doing it. Executive summary This report provides close analysis of the impact of bilateral trade between Australia and China on Australia’s business and economic integration with global value chains. It also extends the findings of previous reports by evaluating the latest flow-on effects of Australia-China trade for the Australian economy right down to the household level. Commissioned by the Australia China Business Council, this report expands on prior versions of the “Benefits to Australian Households of Trade with China Report” which, since 2009, have tracked the benefits to ordinary Australian households from trade with China