deaea 2011 - 1st International Workshop on Data Engineering Applications in Emerging Areas: Health Care Informatics and Energy Informatics

There is a growing number of domains requiring the analysis of large amounts of data. However, it is often the case that a considerable amount of effort is required to preprocess the raw data and make it amenable to analysis. Engineering the data is therefore a very important aspect of Data Mining. The workshop focus on two emergent areas where data engineering is crucial to obtain good results: Health Care and Energy Informatics.

Energy Informatics is the application of information technology to integrate and optimize assets in the energy domain including energy sources, generation and distribution infrastructure, billing and monitoring systems, and consumers. Fueled by environmental concerns, availability of energy sources for future, and a growing global population, confronting and addressing critical challenges in the Energy domain has become the need of hour. A few of the facets of these challenges include identifying newer sources of conventional energy, exploiting renewable and green energy sources, optimizing production and distribution, and understanding and spreading awareness about energy conservation. Comprehensive solutions to these problems requires the coming together of energy experts, computer scientists, and social and behavioral studies experts.

Data analysis and management is at the heart of energy informatics. At a micro level, we are rapidly moving towards an instrumented world, with sensors becoming ubiquitous, from electronic and home appliances to buildings, creating an endless stream of high throughput data. At a macro level, optimal management of production capacity vis-a-vis the consumption load, balancing the load between renewable and non-renewable sources of energy, factoring weather and environmental data in energy production, dynamic pricing, all require robust, scalable, and reliable data analysis and management. Analysis of social adoption and reaction to energy consumption monitoring is critical to ensure adoption at a significant scale. Another aspect of energy informatics that is relevant to data engineering is energy efficient data engineering. As more and more computing moves to the cloud and large data centers are developed, it is pertinent to understand the energy trade-offs in the design of these data centers and make them more power efficient.

Data analysis is also becoming ever more fundamental in health informatics. As an example, the use of computational methods such as computer aided drug design (CADD), permeates all aspects of drug discovery. The industrial and academic laboratories who are most proficient with these computational tools have the advantage of developing new drug candidates in a more rational and efficient way. CADD employs a wide range of computational tools to facilitate and enhance the drug discovery process, such as: chemical similarity search, two to three dimensional (2D to 3D) structure conversion, molecular visualisation, quantum chemistry, molecular mechanics, docking, ADMET prediction and conformational searching.

Data Mining methods have been used to improve the efficiency of in silico methods. One such problems concerns the prediction of ADMET properties of organic compounds. The problem is to identify clear relationships between a molecule’s chemical structure and its ADMET activity. These relationships can be used to build predictive models to apply to new compounds. Ultimately, this task can be regarded as a method to predict Quantitative Structure-Activity Relationships (QSARs).

The workshop aims at providing a forum for researchers and practitioners from industry to exchange findings and review knowledge and understanding on topics related to the data engineering aspects of the two domains addressed.