Dynamic Database Schemas and Multi-Paradigm Persistence Transformations

Today, countless businesses use relational databases to store essential information. That data, however, doesn’t always come in the same structure. XML files, for example, may have various schemas for a document type, outlined by numerous XSD files. It may not always make sense to use a traditional relational database for this storage, as NoSQL solutions offer flexibility, speed, and powerful visualization tools. Often enough these documents and schemas are known and used by numerous branches or offices in a company, but need to be stored in a centrally located database. The goal of this work is to solve the problem of saving XML files of various schema types in the same database, by dynamically altering the schema of the database to accommodate the new file structures. In addition to relational database storage, the XML files are also mapped to a graph database to accommodate additional business needs such as visualizing relationships among the data using more powerful methods than traditional data stores. This project also aims to minimize the effort spent by a software developer persisting data with different schema types as well as time allocated to creating methods for storing newly added schemas to the data persistence workflow. It achieves this by automating the process, using several existing persistence frameworks such as Java Architecture for XML Binding (JAXB), Hibernate Object-Relational Mapping (ORM), and the Neo4J Object Graph Mapping Library (OGM). This work intends to integrate these technologies into a cohesive, easily configurable, highly extensible framework that provides a largely automated solution to dynamically mapping evolving data structures to multiple data persistence paradigms

Managing community membership information in a small-world grid

As the Grid matures the problem of resource discovery across communities, where resources now include computational services, is becoming more critical. The number of resources available on a world-wide grid is set to grow exponentially in much the same way as the number of static web pages on the WWW. We observe that the world-wide resource discovery problem can be modelled as a slowly evolving very-large sparse-matrix where individual matrix elements represent nodes’ knowledge of one another. Blocks in the matrix arise where nodes offer more than one service. Blocking effects also arise in the identification of sub-communities in the Grid. The linear algebra community has long been aware of suitable representations of large, sparse matrices. However, matrices the size of the world-wide grid potentially number in the billions, making dense solutions completely intractable. Distributed nodes will not necessarily have the storage capacity to store the addresses of any significant percentage of the available resources. We discuss ways of modelling this problem in the regime of a slowly changing service base including phenomena such as percolating networks and small-world network effects

An evolutionary behavioral model for decision making

For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based model that tries to deal with this problem is the one proposed by Maes, the Bbehavior Network model. This model proposes a set of behaviors as purposive perception-action units which are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-regulating its own behavior selection process), and despite the fact that several extensions have been proposed in order to improve the original model adaptability, there is not a robust model yet that can self-modify adaptively both the topological structure and the functional purpose\ud of the network as a result of the interaction between the agent and its environment. Thus, this work proffers an innovative hybrid model driven by gene expression programming, which makes two main contributions: (1) given an initial set of meaningless and unconnected units, the evolutionary mechanism is able to build well-defined and robust behavior networks which are adapted and specialized to concrete internal agent's needs and goals; and (2)\ud the same evolutionary mechanism is able to assemble quite\ud complex structures such as deliberative plans (which operate in the long-term) and problem-solving strategies

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Image databases: Problems and perspectives

With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined