Explain challenges brought about by emerging scientific applications of data mining.

  some of the challenges brought about by emerging scientific applications of data mining.

Data warehouses and data preprocessing: Data preprocessing and data warehouses are critical for information exchange and data mining. Creating a warehouse often requires finding means for resolving inconsistent or incompatible data collected in multiple environments and at different time periods. This requires recognizing semantics, referencing systems, geometry, measurements, accuracy, and precision. Methods are needed for integrating data from heterogeneous sources and for identifying events.

For instance, consider climate and ecosystem data, which are spatial and temporal and require cross-referencing geospatial data. A major problem in analyzing such data is that there are too many events in the spatial domain but too few in the temporal domain. For example, El Nino events occur only every four to seven years, and previous data on them might not have been collected as systematically as they are today. Methods are also needed for the efficient computation of sophisticated spatial aggregates and the handling of spatial-related data streams.

 Mining complex data types: Scientific data sets are heterogeneous in nature. They typically involve semi-structured and unstructured data, such as multimedia data and georeferenced stream data, as well as data with sophisticated, deeply hidden semantics (e.g., genomic and proteomic data). Robust and dedicated analysis methods are needed for handling spatiotemporal data, biological data, related concept hierarchies, and complex semantic relationships. For example, in bioinformatics, a research problem is to identify regulatory influences on genes. Gene regulation refers to how genes in a cell are switched on (or off) to determine the cell's functions. Different biological processes involve different sets of genes acting together in precisely regulated patterns. Thus, to understand a biological process we need to identify the participating genes and their regulators. This requires the development of sophisticated data mining methods to analyze large biological data sets for clues la about regulatory influences on specific genes, by finding DNA segments ("regulatory sequences") mediating such influence.

Graph-based and network-based mining: It is often difficult or impossible to model several physical phenomena and processes due to the limitations of existing modeling approaches. Alternatively, labeled graphs and networks may be used to capture many of the spatial, topological, geometric, biological, and other relational characteristics present in scientific data sets. In graph or network modeling, each object to be mined is represented by a vertex in a graph, and edges between vertices represent relationships between objects. For example, graphs can be used to model chemical structures, biological pathways, and data generated by numeric simulations such as fluid-flow simulations. The success of graph or network modeling, however, depends on improvements in the scalability and efficiency of many graph-based data mining tasks such as classification, frequent pattern mining, and clustering.

Visualization tools and domain-specific knowledge: High-level graphical user interfaces and visualization tools are required for scientific data mining systems. These should be integrated with existing domain-specific data and information systems to guide researchers and general users in searching for patterns, interpreting and visualizing discovered patterns, and using discovered knowledge in their decision-making.