University of Tulsa

Displaying 1-9 of 9 results

  • White Papers // May 2013

    Supply Chain Duplicate Transportation RFID Data Stream Filtering

    The supply chain is a system characterized by the mobility between the various processes of the chain as well as within the process themselves. Data capturing, gathering and management technologies are always needed by companies to support their decision-making and plans, develop their strategies and improve their mobility in supply...

    Provided By University of Tulsa

  • White Papers // Jan 2013

    A Generic Knowledge Model for SME Supply Chain Based on Multiagent Paradigm

    The supply chain concept was born in the 90's when management techniques in the business world were evolving from independent to collaborative logistics. It is well known that the supply chain is a complex macro system. This complexity is firstly due to the variety of the involved organizations and the...

    Provided By University of Tulsa

  • White Papers // Aug 2012

    Towards the Next Generation of Data Warehouse Personalization System A Survey and a Comparative Study

    Multidimensional databases are a great asset for decision making. Their users express complex OLAP (On-Line Analytical Processing) queries, often returning huge volumes of facts, sometimes providing little or no information. Furthermore, due to the huge volume of historical data stored in DWs, the OLAP applications may return a big amount...

    Provided By University of Tulsa

  • White Papers // Jul 2012

    Cyclic Association Rules Mining Under Constraints

    Several researchers have explored the temporal aspect of association rules mining. In this paper, the authors focus on the cyclic association rules, in order to discover correlations among items characterized by regular cyclic variation overtime. The overview of the state of the art has revealed the drawbacks of proposed algorithm...

    Provided By University of Tulsa

  • White Papers // Jun 2012

    Building Conflict-Aware Profiling Ontology from Data Warehouses

    User profiles or user models are crucial in many areas in which it is essential to obtain knowledge about users of software applications such as data warehouse technologies. To enhance the personalized services, group profiles are derived through combining individual user profiles in order to represent group modelling. In this...

    Provided By University of Tulsa

  • White Papers // May 2012

    Implementation of an Intrusion Detection System

    Securing networks and data is among interesting issues of computer science research and practice. Many approaches and techniques have been developed to secure computer architectures, they addressed several layers, e.g, physical security, applications and encryption algorithms, etc. In this paper, the authors address the problem of securing large networks with...

    Provided By University of Tulsa

  • White Papers // Apr 2012

    Frequent Patterns Mining in Time-Sensitive Data Stream

    Mining frequent itemsets through static Databases has been extensively studied and used and is always considered a highly challenging task. For this reason it is interesting to extend it to data streams field. In the streaming case, the frequent patterns' mining has much more information to track and much greater...

    Provided By University of Tulsa

  • White Papers // Jan 2012

    Comparing Reputation Schemes for Detecting Malicious Nodes in Sensor Networks

    Remotely deployed sensor networks are vulnerable to both physical and electronic security breaches. The sensor nodes, once compromised, can send erroneous data to the base station, thereby possibly compromising network effectiveness. The authors assume that sensor nodes are organized in a hierarchy and use offline neural network based learning technique...

    Provided By University of Tulsa

  • White Papers // Jan 2010

    Access Policy Specification for SCADA Networks

    Efforts to secure Supervisory Control And Data Acquisition (SCADA) systems must be supported under the guidance of sound security policies and mechanisms to enforce them. Critical elements of the policy must be systematically translated into a format that can be used by policy enforcement components. Ideally, the goal is to...

    Provided By University of Tulsa

  • White Papers // Apr 2012

    Frequent Patterns Mining in Time-Sensitive Data Stream

    Mining frequent itemsets through static Databases has been extensively studied and used and is always considered a highly challenging task. For this reason it is interesting to extend it to data streams field. In the streaming case, the frequent patterns' mining has much more information to track and much greater...

    Provided By University of Tulsa

  • White Papers // Aug 2012

    Towards the Next Generation of Data Warehouse Personalization System A Survey and a Comparative Study

    Multidimensional databases are a great asset for decision making. Their users express complex OLAP (On-Line Analytical Processing) queries, often returning huge volumes of facts, sometimes providing little or no information. Furthermore, due to the huge volume of historical data stored in DWs, the OLAP applications may return a big amount...

    Provided By University of Tulsa

  • White Papers // Jul 2012

    Cyclic Association Rules Mining Under Constraints

    Several researchers have explored the temporal aspect of association rules mining. In this paper, the authors focus on the cyclic association rules, in order to discover correlations among items characterized by regular cyclic variation overtime. The overview of the state of the art has revealed the drawbacks of proposed algorithm...

    Provided By University of Tulsa

  • White Papers // Jun 2012

    Building Conflict-Aware Profiling Ontology from Data Warehouses

    User profiles or user models are crucial in many areas in which it is essential to obtain knowledge about users of software applications such as data warehouse technologies. To enhance the personalized services, group profiles are derived through combining individual user profiles in order to represent group modelling. In this...

    Provided By University of Tulsa

  • White Papers // May 2012

    Implementation of an Intrusion Detection System

    Securing networks and data is among interesting issues of computer science research and practice. Many approaches and techniques have been developed to secure computer architectures, they addressed several layers, e.g, physical security, applications and encryption algorithms, etc. In this paper, the authors address the problem of securing large networks with...

    Provided By University of Tulsa

  • White Papers // Jan 2013

    A Generic Knowledge Model for SME Supply Chain Based on Multiagent Paradigm

    The supply chain concept was born in the 90's when management techniques in the business world were evolving from independent to collaborative logistics. It is well known that the supply chain is a complex macro system. This complexity is firstly due to the variety of the involved organizations and the...

    Provided By University of Tulsa

  • White Papers // May 2013

    Supply Chain Duplicate Transportation RFID Data Stream Filtering

    The supply chain is a system characterized by the mobility between the various processes of the chain as well as within the process themselves. Data capturing, gathering and management technologies are always needed by companies to support their decision-making and plans, develop their strategies and improve their mobility in supply...

    Provided By University of Tulsa

  • White Papers // Jan 2010

    Access Policy Specification for SCADA Networks

    Efforts to secure Supervisory Control And Data Acquisition (SCADA) systems must be supported under the guidance of sound security policies and mechanisms to enforce them. Critical elements of the policy must be systematically translated into a format that can be used by policy enforcement components. Ideally, the goal is to...

    Provided By University of Tulsa

  • White Papers // Jan 2012

    Comparing Reputation Schemes for Detecting Malicious Nodes in Sensor Networks

    Remotely deployed sensor networks are vulnerable to both physical and electronic security breaches. The sensor nodes, once compromised, can send erroneous data to the base station, thereby possibly compromising network effectiveness. The authors assume that sensor nodes are organized in a hierarchy and use offline neural network based learning technique...

    Provided By University of Tulsa