Distributed Information Sources Research Articles

Remote Tracking of Distributed Dynamic Sources Over a Random Access Channel With One-Bit Updates

In this work, we consider a network, where distributed information sources whose states evolve according to a random process transmit their time-varying states to a remote estimator over a shared wireless channel. Each source generates packets in a decentralized manner and employs a slotted random access mechanism to transmit the packets. In particular, we are interested in networks with a large number of low-complexity devices that share low-capacity random access channels. Accordingly, we investigate update strategies for remote tracking of source states that require each update to constitute as few bits as possible. To that end, we develop update strategies requiring only one-bit of information per update that employ a local cancellation strategy. We further analytically compare the performance of the cancellation-enabled update policy to the optimal policy that does not restrict the number of bits for each update, which show that an asymptotic upper bound of the optimality ratio is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\frac{13\sqrt{2}}{12}$</tex-math></inline-formula> . Through simulations, we compare the proposed cancellation-enabled one-bit update policy with zero-wait sampling and threshold-based sampling policies that require more than one-bit of information per update. The comparisons show that the cancellation-enabled update policy at its optimal threshold level outperforms the multi-bit update policies.

Flexible Data Refreshing Architecture for Health Information System Integration

Background: Having a consistent and unified view of heterogeneous distributed medical information sources is an inevitable need of health informatics. Integrating medical information of patients or about a disease, a treatment or side effects of a drug, etc, is very useful to help medical education, to achieve medical research goals and to provide the computer- based decision support systems. Contribution: This article proposes a flexible incremental update method for the materialized part of the integration system. It permits us to manage the integration system according to the characteristics of the data sources which can change. Method: This paper presents a hybrid data integration approach in which the materialized part of the system in mediator is the object indexation structure based on an instance classification of the sources objects which correspond to the global schema. The object identifier of each object in the indexation structure is materialized together with the attributes which are needed for the incremental updating of this indexation (classifying attributes). Results: The main idea of this paper is to develop a hybrid data integration framework, which represents a new aspect of a hybrid method focusing on flexible data refreshing. Conclusion: This hybrid approach implements a vertical hybrid approach. It means that at the mediator level, some data of each object are materialized and others are virtual.

Joint Compression and Encryption of Distributed Sources Based on Wavelet Transform and Semi-Tensor Product Compressed Sensing

Aiming at the shortage of data transmission security and efficiency in wireless distributed transient pressure testing system, a new distributed information source joint compression and encryption scheme is proposed in this paper by combining wavelet transform, semi-tensor product compressed sensing and hash chain. Firstly, the signals are decomposed into different components by wavelet transform. Then, the measurement matrix of semi-tensor product compressed sensing is updated with the hash chain related to plaintext, and its used to compress and encrypt the different wavelet components. Finally, the measurement results of the previous steps are scrambled by hash sequences for further encryption. In this paper, simulation test is carried out by using the shockwave signal measured in practice. The simulation results show that the scheme has good reconstruction accuracy and security. When the compression rate reaches 0.25, the scheme can reduce the storage space and the computing resources required by the measurement matrix to 1/32 and 1/8 of traditional distributed compressed sensing. The proposed scheme is a flexible and efficient joint compression and encryption framework, which can flexibly adjust the measurement number of different types of components to ensure important information is effectively measured, balance reconstruction quality, compression rate and system resource consumption.

A longitudinal study of topic classification on Twitter.

Twitter represents a massively distributed information source over topics ranging from social and political events to entertainment and sports news. While recent work has suggested this content can be narrowed down to the personalized interests of individual users by training topic filters using standard classifiers, there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? In addition, how robust is a topic classifier over the time horizon, e.g., can a model trained in 1 year be used for making predictions in the subsequent year? Furthermore, what features, feature classes, and feature attributes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 diverse themes ranging from social issues to celebrity deaths to the “Iran nuclear deal”. The results of this long-term study of topic classifier performance provide a number of important insights, among them that: (i) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training though performance degrades with time, (ii) the classes of hashtags and simple terms contain the most informative feature instances, (iii) removing tweets containing training hashtags from the validation set allows better generalization, and (iv) the simple volume of tweets by a user correlates more with their informativeness than their follower or friend count. In summary, this work provides a long-term study of topic classifiers on Twitter that further justifies classification-based topical filtering approaches while providing detailed insight into the feature properties most critical for topic classifier performance.

A Hierarchical Hadoop Framework to Handle Big Data in Geo-Distributed Computing Environments

Advances in the communication technologies, along with the birth of new communication paradigms leveraging on the power of the social, has fostered the production of huge amounts of data. Old-fashioned computing paradigms are unfit to handle the dimensions of the data daily produced by the countless, worldwide distributed sources of information. So far, the MapReduce has been able to keep the promise of speeding up the computation over Big Data within a cluster. This article focuses on scenarios of worldwide distributed Big Data. While stigmatizing the poor performance of the Hadoop framework when deployed in such scenarios, it proposes the definition of a Hierarchical Hadoop Framework (H2F) to cope with the issues arising when Big Data are scattered over geographically distant data centers. The article highlights the novelty introduced by the H2F with respect to other hierarchical approaches. Tests run on a software prototype are also reported to show the increase of performance that H2F is able to achieve in geographical scenarios over a plain Hadoop approach.

A Longitudinal Study of Topic Classification on Twitter

Twitter represents a massively distributed information source over a kaleidoscope of topics ranging from social and political events to entertainment and sports news. While recent work has suggested that variations on standard classifiers can be effectively trained as topical filters (Lin, Snow, and Morgan 2011; Yang et al. 2014; Magdy and Elsayed 2014), there remain many open questions about the efficacy of such classification-based filtering approaches. For example, over a year or more after training, how well do such classifiers generalize to future novel topical content, and are such results stable across a range of topics? Furthermore, what features and feature classes are most critical for long-term classifier performance? To answer these questions, we collected a corpus of over 800 million English Tweets via the Twitter streaming API during 2013 and 2014 and learned topic classifiers for 10 diverse themes ranging from social issues to celebrity deaths to the “Iran nuclear deal”. The results of this long-term study of topic classifier performance provide a number of important insights, among them that (1) such classifiers can indeed generalize to novel topical content with high precision over a year or more after training and (2) simple terms and locations are the most informative feature classes (despite training on classes labeled via hashtags).

AmePLM: a platform providing information provision in engineering

The procedure for the engineering of products and production systems in manufacturing companies is often distributed over several organisational units. The different units involved in these procedures use their respective methods and tools, resulting in silo-like and distributed information and data sources for engineering. In this paper, the open platform amePLM is introduced. This platform is based on a semantic data model. The ontology as explicitly formulated data model allows an integrated view on data and information available in the systems used along the product lifecycle, and the automatic provision of suitable information to the user. Furthermore, this open approach allows the linking of the solution to existing engineering software systems in the sense of a continuous flow of information.

Integrating distributed sources of information for construction cost estimating using Semantic Web and Semantic Web Service technologies

Abstract A construction project requires collaboration of several organizations such as owner, designer, contractor, and material supplier organizations. These organizations need to exchange information to enhance their teamwork. Understanding the information received from other organizations requires specialized human resources. Construction cost estimating is one of the processes that requires information from several sources including a building information model (BIM) created by designers, estimating assembly and work item information maintained by contractors, and construction material cost data provided by material suppliers. Currently, it is not easy to integrate the information necessary for cost estimating over the Internet. This paper discusses a new approach to construction cost estimating that uses Semantic Web technology. Semantic Web technology provides an infrastructure and a data modeling format that enables accessing, combining, and sharing information over the Internet in a machine processable format. The estimating approach presented in this paper relies on BIM, estimating knowledge, and construction material cost data expressed in a web ontology language. The approach presented in this paper makes the various sources of estimating data accessible as Simple Protocol and Resource Description Framework Query Language (SPARQL) endpoints or Semantic Web Services. We present an estimating application that integrates distributed information provided by project designers, contractors, and material suppliers for preparing cost estimates. The purpose of this paper is not to fully automate the estimating process but to streamline it by reducing human involvement in repetitive cost estimating activities.

Improving Emergency Response Systems Through the Use of Intelligent Information Systems

The shifts experienced in the environment of disaster operations and emergency response are reshaping the context of information acquisition and utilization in hospitals. In addition to the formulation of emergency response plans, healthcare organizations (especially hospitals) are being challenged by the growing need to maintain and share real time information necessary for the improvement of emergency response processes. Such information-related attention originates from many emergency-specific concerns including the limited focus of current emergency response information systems, decision limitations that challenge the formulation of decision support applications and the characterization of user requirements, the heterogeneity of emergency response information and the difficulty of integrating spatially distributed information sources. The increased attention in emergency response information also emerges from the recent technological developments (in terms of hardware, software functionalities, databases and telecommunication, among others) which significantly affected the processing, storage and retrieval of real time information. This paper focuses on the examination of the context of emergency response in Al Ahsaa area of Saudi Arabia and the applicability of multi-agent information systems through the proposal of an integrated architecture. Then it sheds light on the implementation concerns to ensure the contribution of the proposed architecture towards the engagement of stakeholders, the improvement of the availability and accessibility of emergency management information and the harmonization of emergency response operations. .

Service computing and agents paradigm for managing distributed information sources

Information gathered from several distributed and interconnected sources is subject to high complexity and may lack relevance if not considered as a whole, with a systemic view. In this context, information source content is strongly related to other information sources contents to which it is connected. The content itself may have several meanings following its use purpose. And finally, the way information sources are connected to each other, may have an important impact on the gathered information. A model is proposed, where its highlighting point is to contribute to information system agility by defining a three-dimensional view considering content, use and structure in the same framework. This exhibits information complexity and the interest of methodologies based on self-organised principles to manage the complexity. The contribution is mainly focused on presenting some factors that lead to self-organisation in a service-oriented architecture SOA. It shows how it can be possible, through agents' paradigm, to integrate the associated mechanisms in this context.

Prototype for Extended XDB Using Wiki

This paper describes a prototype of extended XDB. XDB is an open-source and extensible database architecture developed by National Aeronautics and Space Administration (NASA) to provide integration of heterogeneous and distributed information resources for scientific and engineering applications. XDB enables an unlimited number of desktops and distributed information sources to be linked seamlessly and efficiently into an information grid using Data Access and Retrieval Composition (DARC) protocol which provides a contextual search and retrieval capability useful for lightweight web applications. This paper shows the usage of XDB on common data management in the enterprise without burdening users and application developers with unnecessary complexity and formal schemas. Supported by NASA Ames Research Center through NASA Exploration System Mission Directorate (ESMD) Higher Education grant, a project team at Fairfield University extended this concept and developed an extended XDB protocol and a prototype providing text-searches for Wiki. The technical specification of the protocol was posted to Source Forge (sourceforge.net) and a prototype providing text-searches for Wiki was developed. The prototype was created for 16 tags of the MediaWiki dialect. As part of future works, the prototype will be further extended to the complete Wiki markups and other dialects of Wiki.

A Multimodal Approach to Incremental User Profile Building

The World Wide Web is the largest distributed information source which is accessed by billions of people all across the world. A unique content so urce on the web can be accessed byvarious users for different purposes. Hence it becomes mandatory tocapture specific information requirements of each user. This paper proposes a multimodal approach to build profile of users in a n incremental manner. The approach proposed in this paper achieves the goal of building user profiles using a hybrid approach. The profile building process is further enriched with the incorporation of web page segmentation technique involving page trees and densitometry. The proposed model extracts the requirement context of the user by utilizing both local and global sources during the profile building process.The experiments conducted on the proposed model confirm the effectiveness of the approach and highlights the channels wh ich carries an edge over others in capturing the user interest.

A multi-sources knowledge management system

Abstract Nowadays the development of a product involves different types of actors (employees, managers, board of directors) which must be able to share knowledge, experiences and work together efficiently. Each actor has a professional specialty and use one or several software tools (CAO, project management, PLM tools …) dedicated to his specific skills. Each of these software tools produces different information sources (databases, XML files, text files) which are distributed through the enterprise network. We present in this paper a Knowledge Management System (KMS) which allows the capitalization of the distributed and heterogeneous knowledge all along the development of mechanical product projects on the basis of the heterogeneous and distributed information sources.

The Query and Integration for Distributed Resource Description Framework

RDF(S) can be used to semantically describe the distributed information sources in enterprises to make the information retrieval more accurate. A distributed RDF(S) model is proposed to describe distributed and heterogeneous RDF(S)s. Based on this model, a method to query distributed RDF(S) descriptions is also presented, which can retrieve data of concepts as well as that of instances. The query method can not only applied in the instance level, but also in the concept level. By this method, user can query relevant information resources in a unified form and implement distributed RDF(S)’s integration.

Agent paradigm and services technology for distributed Information Sources

The complexity of information is issued from interacting information sources (IS), and could be better exploited with respect to relevance of information. In distributed IS system, relevant information has a content that is in connection with other contents in information network, and is used for a certain purpose. The highlighting point of the proposed model is to contribute to information system agility according to a three-dimensional view involving the content, the use and the structure. This reflects the relevance of information complexity and effective methodologies through self organized principle to manage the complexity. This contribution is primarily focused on presenting some factors that lead and trigger for self organization in a Service Oriented Architecture (SOA) and how it can be possible to integrate self organization mechanism in the same.

@neurIST: Infrastructure for Advanced Disease Management Through Integration of Heterogeneous Data, Computing, and Complex Processing Services

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

An ActOn-based semantic information service for Grids

We describe a semantic information service that aggregates metadata from a large number of information sources of a large-scale Grid infrastructure. It uses an ontology-based information integration architecture (ActOn) suitable for the highly dynamic distributed information sources available in Grid systems, where information changes frequently and where the information of distributed sources has to be aggregated in order to solve complex queries. These two challenges are addressed by a Metadata Cache that works with an update-on-demand policy and by an information source selection module that selects the most suitable source at a given point in time. We have evaluated the quality of this information service, and compared it with other similar services from the EGEE production testbed, with promising results.

Means and structure of visual-optical replication of distributed sources of technological information

Means and structure of visual-optical replication of distributed sources of technological information

A multiagent framework for coordinated parallel problem solving

Today's organizations, under increasing pressure on the effectiveness and the increasing need for dealing with complex tasks beyond a single individual's capabilities, need technological support in managing complex tasks that involve highly distributed and heterogeneous information sources and several actors. This paper describes CoPSF, a multiagent system middle-ware that simplifies the development of coordinated problem solving applications while ensuring standard compliance through a set of system services and agents. CoPSF hosts and serves multiple concurrent teams of problem solving contributing both to the limitation of communication overheads and to the reduction of redundant work across teams and organizations. The framework employs (i) an interleaved task decomposition and allocation approach, (ii) a mechanism for coordination of agents' work, and (iii) a mechanism that enables synergy between parallel teams.

Towards refinement of abductive or inductive hypotheses through propagation

In this paper we address the problem of distributed sources of information, or agents, that observe the environment locally and have to communicate in order to refine their hypothesis regarding the actual state of this environment. One way to address the problem would be to centralize all the collected observations and knowledge, and to centrally compute the resulting theory. In many situations however, it would not be possible to adopt this centralized approach (e.g. for practical reasons, or privacy concerns). In this paper, we assume that agents individually face abductive or inductive tasks in a globally coherent environment, and we show that general mechanisms can be designed that abstractly regard both cases as special instances of a problem of hypothesis refinement through propagation. Assuming that agents are equipped with some individual revision machinery, our concern will be to investigate how (under what conditions) convergence to a consistent state can be guaranteed at more global levels: (i) between two agents; (ii) in a clique of agents; and (iii) in general in a connected society of agents.