Learning k-Nearest Neighbors Classifier from Distributed Data

Most learning algorithms assume that all the relevant data are available on a single computer site. In the emerging networked environments learning tasks are encountering situations in which the relevant data exists in a number of geographically distributed databases that are connected by communication networks. These databases cannot be moved to other network sites due to security, size, privacy, or data-ownership considerations. In this paper we show how a k-nearest classifier algorithm can be adapted for distributed data situations. The objective of our algorithms is to achieve the learning objectives for any data distribution encountered across the network by exchanging local summaries among the participating nodes

Government Transparency: Six Strategies for More Open and Participatory Government

Offers strategies for realizing Knight's 2009 call for e-government and openness using Web 2.0 and 3.0 technologies, including public-private partnerships to develop applications, flexible procurement procedures, and better community broadband access

Architecture for Provenance Systems

This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies

An Architecture for Provenance Systems

This document covers the logical and process architectures of provenance systems. The logical architecture identifies key roles and their interactions, whereas the process architecture discusses distribution and security. A fundamental aspect of our presentation is its technology-independent nature, which makes it reusable: the principles that are exposed in this document may be applied to different technologies

The use of ontologies for effective knowledge modelling and information retrieval

© 2017 The dramatic increase in the use of knowledge discovery applications requires end users to write complex database search requests to retrieve information. Such users are not only expected to grasp the structural complexity of complex databases but also the semantic relationships between data stored in databases. In order to overcome such difficulties, researchers have been focusing on knowledge representation and interactive query generation through ontologies, with particular emphasis on improving the interface between data and search requests in order to bring the result sets closer to users research requirements. This paper discusses ontology-based information retrieval approaches and techniques by taking into consideration the aspects of ontology modelling, processing and the translation of ontological knowledge into database search requests. It also extensively compares the existing ontology-to-database transformation and mapping approaches in terms of loss of data and semantics, structural mapping and domain knowledge applicability. The research outcomes, recommendations and future challenges presented in this paper can bridge the gap between ontology and relational models to generate precise search requests using ontologies. Moreover, the comparison presented between various ontology-based information retrieval, database-to-ontology transformations and ontology-to-database mappings approaches provides a reference for enhancing the searching capabilities of massively loaded information management systems

Active databases, business rules and reactive agents - what is the connection?

These three technologies were and still are mainly treated separately. Since not much work has been carried out in defining and combining them together, we are going to present what has been done and put accent on what could be done. Namely, they rely upon similar paradigms and concepts, as will be shown later on, and can be treated as complementary technologies. In this paper we will show that reactive agents react according to some set of business rules and active databases can be used as a suitable means for implementing business rules and in those way reactive agents as well. Since reactive agents have been well defined, recent improvements in the fields of active databases technology and especially business rules provide the reason to consider the benefits to be achieved from combining these fields

Active databases, business rules and reactive agents - what is the connection?

These three technologies were and still are mainly treated separately. Since not much work has been carried out in defining and combining them together, we are going to present what has been done and put accent on what could be done. Namely, they rely upon similar paradigms and concepts, as will be shown later on, and can be treated as complementary technologies. In this paper we will show that reactive agents react according to some set of business rules and active databases can be used as a suitable means for implementing business rules and in those way reactive agents as well. Since reactive agents have been well defined, recent improvements in the fields of active databases technology and especially business rules provide the reason to consider the benefits to be achieved from combining these fields

An Architectural Approach to Ensuring Consistency in Hierarchical Execution

Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of knowledge can lead to difficulty in maintaining logical consistency in asserted knowledge. We explore the problematic consequences of persistent assumptions in the reasoning process and introduce novel potential solutions. Having implemented one of the possible solutions, Dynamic Hierarchical Justification, its effectiveness is demonstrated with an empirical analysis

Optimal Unviersal Schedules for Discrete Broadcast

In this paper we study the scenario in which a server sends dynamic data over a single broadcast channel to a number of passive clients. We consider the data to consist of discrete packets, where each update is sent in a separate packet. On demand, each client listens to the channel in order to obtain the most recent data packet. Such scenarios arise in many practical applications such as the distribution of weather and traffic updates to wireless mobile devices and broadcasting stock price information over the Internet. To satisfy a request, a client must listen to at least one packet from beginning to end. We thus consider the design of a broadcast schedule which minimizes the time that passes between a clients request and the time that it hears a new data packet, i.e., the waiting time of the client. Previous studies have addressed this objective, assuming that client requests are distributed uniformly over time. However, in the general setting, the clients behavior is difficult to predict and might not be known to the server. In this work we consider the design of universal schedules that guarantee a short waiting time for any possible client behavior. We define the model of dynamic broadcasting in the universal setting, and prove various results regarding the waiting time achievable in this framework