Future developments

From the previous paragraphs, we can see that we are already witnessing various grid-based data mining research and development activities. There are many potential extensions of this work towards comprehensive, productive and high-performance analysis of scientific data sets.

Below we outline three promising future research avenues.

3.6.1 High-level data mining model

The most popular database technologies, like the relational one, have precise and clear founda-tions in the form of uniform models and algebraic and logical frameworks on which significant theoretical and system research has been conducted. The emergence of the relational query language SQL and its query processors is an example of this development. A similar state has to be achieved in data mining in order for it to be leveraged as a successful technology and science. The research in this field will include three main steps: (i) identifying the properties required of such a model in the context of large-scale data mining, (ii) examining the currently available models that might contribute (e.g. CRISP-DM) and (iii) developing a formal notation that exposes the model features.

3.6.2 Data mining query language

From a user’s point of view, the execution of a data mining process and the discovery of a set of patterns can be considered as either the result of an execution of a data mining workflow or an answer to a sophisticated database query. The first is called the procedural approach, while the second is the descriptive approach. GridMiner and other grid-based data mining developments are based on this model. To support the descriptive approach several languages have been developed. The Data Mining Query Language (DMQL) (Han, 2005) and OLE DB for Data Mining (Netz, et al., 2001) are good examples. A limitation of these languages is their poor support for data preparation, transformation and post-processing. We believe that the design of such a language has to be based on an appropriate data mining model; its features will be reflected by the syntactic and semantic structure of the language. Moreover, implementation of queries expressed in the language on the grid, which represents a highly volatile distributed environment, will require investigation of dynamic approaches able to sense the query execution status and grid status in specified time intervals and appropriately adapt the query execution plan.

3.6.3 Distributed mining of data streams

In the past five years with advances in data collection and generation technologies, a new class of application has emerged that requires managing data streams, i.e. data composed of continuous, real time sequence of items. A significant research effort has already been devoted to stream data management (Chaudhry, Shaw and Abdelguerfi, 2005) and data stream mining (Aggarwal, 2007). However, in advanced applications there is a need to mine multiple data streams. Many systems use a centralized model (Babcock, et al., 2002). Here, the distributed data streams are directed to one central location before they are mined. Such a model is limited in many aspects. Recently, several researchers have proposed a distributed model considering distributed data sources and computational resources–an excellent survey was provided by

REFERENCES 53 Parthasarathy, Ghoting and Otey (2007). We believe that investigation of grid-based distributed mining of data streams is also an important future research direction.

3.7 Conclusions

The characteristics of data exploration in modern scientific applications impose unique require-ments for analytical tools and services as well as their organization into scientific workflows.

In this chapter, we have introduced our approach to the e-science analytics that has been in-corporated into the GridMiner framework. The framework aims to exploit modern software engineering and data engineering methods based on service-oriented Web and grid technolo-gies. An additional goal of the framework is to give the data mining expert powerful support to achieve appealing results. There are two main issues driving the development of such an infrastructure. The first is the increasing volumes and complexity of involved data sets, and the second is the heterogeneity and geographic distribution of these data sets and the scientists who want to analyse them. The GridMiner framework attempts to address both challenges, and we believe successfully. Several data mining services have been already deployed and are ready to perform the knowledge discovery tasks and OLAP.

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ADaM services: Scientific data