Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments

An Innovative, Multidisciplinary Educational Program in Interactive Information Storage and Retrieval

There exists a large number of large-scale bibliographic Information Storage and Retrieval Systems containing large amounts of valuable data of interest in a wide variety of research applications. These systems are not used to capacity because the end users, i.e., the researchers, have not been trained in the techniques of accessing such systems. This thesis describes the development of a transportable, university-level course in methods of querying on-line interactive Information Storage and Retrieval systems as a solution to this problem. This course was designed to instruct upper division science and engineering students to enable these end users to directly access such systems. The course is designed to be taught by instructors who are not specialists in either computer science or research skills. It is independent of any particular IS and R system or computer hardware. The project is sponsored by NASA and conducted by the University of Southwestern Louisiana and Southern University

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Delay Tolerant Networking over the Metropolitan Public Transportation

We discuss MDTN: a delay tolerant application platform built on top of the Public Transportation System (PTS) and able to provide service access while exploiting opportunistic connectivity. Our solution adopts a carrier-based approach where buses act as data collectors for user requests requiring Internet access. Simulations based on real maps and PTS routes with state-of-the-art routing protocols demonstrate that MDTN represents a viable solution for elastic nonreal-time service delivery. Nevertheless, performance indexes of the considered routing policies show that there is no golden rule for optimal performance and a tailored routing strategy is required for each specific case

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures