1. Research team members http://www.ceti.cse.ohio-state.edu/ Adaptive Complex Enterprise Data Warehousing Repository Generation Semantic Web Knowledge Extraction RDF Ontologies Enterprise Architecture Business Modeling Architecture Frameworks Component Business Model © TOGAF Agent-Based Modeling Enterprise Visualization Heat Maps Workflows Component Business Model Starlight Visualizations Develop insights Design strategy Investment Planning Decision Support Analyzing Complex Service-Oriented Enterprises Feed semantically rich data Analyze data using appropriate techniques Use results to get possible actions Take appropriate actions that serve as a feedback into the business  Enterprises today need to be agile: they must rapidly adapt to changes.  To achieve this goal, they must have a thorough understanding of their current state through different data sources, model this data using the right business model, and visualize the current state effectively.  Based on this understanding, the right strategy can be developed to achieve desired changes in the enterprise.  We are developing an ensemble of techniques so this process can be performed dynamically, proactively, and across multiple layers of granularity in enterprise role. Concept Work Being Done Acknowledgements Thanks to sponsors and to Prof. Jay Ramanathan and Prof. Rajiv Ramnath at CETI. Enterprise Visualization  Large enterprises are characterized by numerous interactions which deliver services to internal, as well as external, business units.  It is vital to develop a holistic view of large enterprises which allow decision making members to gauge relative value of organizational subsets.  The visualization should be capable of dynamically updating, by using incoming information from heterogeneous data sources.  Such visualization facilitates conclusions about co-ordination amongst business units, use of shared resources, and their effect on operational output. Work at City of Columbus  We collected data about available resources and missing services for each department.  Once data was available, the city’s services were analyzed by using an activity-based costing.  We developed a transaction-based model which analyzed the service requests in a dynamic fashion by using the service request log available to the city.  ResearchIQ is an initiative of the bio- informatics group at the OSUMC. The goal: a semantically-anchored search engine usable by clinical and translational research communities. MetaDB  The MetaDB initiative looks at role-based access control mechanisms using ontological structures. Data Warehousing  Building an intelligent data warehouse is essential in enterprise analysis.  Extract, transform and load (ETL) is a process in data warehousing which provides foundation for knowledge-based applications: Extracting data from outside sources Transforming it to fit operational needs Loading it into the end target  We look at semantic web technologies as a solution for providing the required level of sophistication. Ohio Department of Jobs and Family Services  A case study with ODJFS demonstrates the utility of a software solution called the ACE Real-Time Monitoring Tool for effective performance management.  By applying machine learning methods, the tool may be taught which performance areas are important for each agent, and learn to balance the needs of different agents. Enterprise Architecture  Large enterprises involve a number of different agent roles, often competing goals (e.g. accounting wants reduced cost, developers want consistency in design, end-users want improved quality). Any tool for decision-making in complex enterprises must feature an agent-based modeling approach.  Enterprises utilize overwhelming amounts of data, more than any employee can examine. Effective tools are needed for pro-active decision making.