Genomic data is paving the way towards personalized healthcare. By unveiling genetic disease-contributing factors, genomic data can aid in the detection, diagnosis, and treatment of a wide range of complex diseases. Integrating genomic data into healthcare is riddled with a wide range of challenges spanning social, ethical, legal, educational, economic, and technical aspects. Bioinformatics is a core integration aspect presenting an overwhelming number of unaddressed challenges. In this paper we tackle the fundamental bioinformatics integration concerns including: genomic data generation, storage, representation, and utilization in conjunction with clinical data. We divide the bioinformatics challenges into a series of seven intertwined integration aspects spanning the areas of informatics, knowledge management, and communication. For each aspect, we provide a detailed discussion of the current research directions, outstanding challenges, and possible resolutions. This paper seeks to help narrow the gap between the genomic applications, which are being predominantly utilized in research settings, and the clinical adoption of these applications.

Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.

Data warehousing (DW) has emerged as one of the most powerful technology innovations in recent years to support organization-wide decision making and has become a key component in the information technology (IT) infrastructure. Proponents of DW claim that its infusion can dramatically enhance the ability of businesses to improve the access, distribution, and sharing of information and provide managerial decision support for complex business questions. DW is also an enabling technology for data mining, customer-relationship management, and other business-intelligence applications. Although data warehouses have been around for quite some time, they have been plagued by high failure rates and limited spread or use. Drawing upon past research on the adoption and diffusion of innovations and on the implementation of information systems (IS), we examine the key organizational and innovation factors that influence the infusion (diffusion) of DW within organizations and also examine if more extensive infusion leads to improved organizational outcomes. In this paper, we conducted a field study, where two senior managers (one from IS and the other from a line function) from 117 companies participated, and developed a structural model to test the research hypotheses. The results indicate that four of the seven variables examined in this paper-organizational support, quality of the project management process, compatibility, and complexity-significantly influence the degree of infusion of DW and that the infusion, in turn, significantly influences organization-level benefits and stakeholder satisfaction. The findings of this paper have interesting implications for both research and practice in IT and DW infusion, as well as in the organization-level impact of the infusion of enterprise-wide infrastructural and decision support technologies such as DW.

A data warehouse is developed using a data warehousing process (DWP) methodology. Currently, there are a large number of methodologies available in the data warehousing market. The reason for this is the lack of any centralized attempts at creating platform-independent DWP standards. For the development of such standards, it is very important that we first examine the current practices being followed by the data warehousing industry. In this study, we review 30 commercial data warehousing methodologies and analyze the standard practices they have adopted with respect to DWP. To perform the analysis, we first develop an ontological model of DWP based on a thorough review of the literature and inputs from experts in the data warehousing field. The ontological model consists of two hierarchies: a composition hierarchy which shows the decomposition of DWP tasks such as system development, extract, transform, and load (ETL), and end-user application design; and a classification hierarchy which specifies the alternative methods or techniques available for performing the tasks. We next apply hierarchical cluster analysis to group the methodologies that share a common set of standards. Our study provides valuable insights into the prevailing standard practices for different DWP tasks-system development, requirements analysis, architecture design, data modeling, ETL, data extraction, and end-user application design-and identifies important directions for future research on DWP standardization

Software design involves translating a set of task requirements into a structured description of a computer program that will perform the task. A software designer can use design schema, collaborative design knowledge, or can reuse design artifacts. Very little has been done to include reuse of design artifacts in the software development life cycle, despite tremendous promises of reuse. As a result, this technique has not seen widespread use, possibly due to a lack of cognitive understanding of the reuse process. This research explores the role of a specific cognitive aspect, opportunism, in demand-side software reuse. We propose a cognitive model based on opportunism that describes the software design process with reuse. Protocol analysis verifies that the software design with reuse is indeed opportunistic and reveals that some software designers employ certain tasks of the reuse process frequently. Based on these findings, we propose a reuse support system that incorporates blackboard technology and existing reuse library management system.

Typically, most of the current decision support systems (DSSs) do not possess a module to help users formulate mathematical programming models. This observation has led researchers to offer different approaches to the design of formulation consultants. Generally, these approaches emphasize the automation of formulation tasks with a predetermined (static) control or guidance strategy. A new approach to constructing a formulation consultant that emphasizes dynamic formulation control, opportunistic control, is offered. A system, AEROBA, has been developed using a blackboard architecture to provide support to the formulation process. Several of the issues raised regarding model formulation, opportunistic control, and blackboard systems are addressed using the AEROBA implementation. The contributions of this research are: (1) understanding the process of model formulation; (2) addressing the concerns of model formulation in attempting to automate the process; and (3) using the blackboard paradigm to deal with the unique characteristics of the model formulation process.>

There is an increasing demand for database management systems to support nontraditional applications, such as computer aided design (CAD) and decision support systems (DSSs), which deal with complex objects. Complex objects are objects that have nonatomic components (unlike the traditional relational model). Many researchers have designed object systems to support complex objects. Five levels of scheme representations are identified: (1) record-structure level (lowest), (2) relational level, (3) logical level, (4) semantic level, and (5) object level (highest). Different object schemes proposed in the literature differ in their levels of representation. The authors discuss the need for a formalism for the object level of representation and describe a formalism for an object scheme called NEXUS.>

A decision support process (DSP), defined as an approach to decision support, uses a programming environment to aid the decision-maker. The DSP is discussed and a system architecture using a hierarchical planning approach to support DSP activities is proposed. These stages include problem solving, solution planning, tools integration, and model execution. A comparative study of DSP research with respect to these stages is made. Using a conceptual framework for DSP as the basis for proposing a system architecture for assisting DSP, the design and development concerns based on hierarchical planning and an attribute-based approach are outlined, and the system, called XDSP (expert decision support process), is described.>