Analyst-Ready Large Scale Real Time Information Retrieval Tool for E-Governance

2009 ◽  
pp. 268-294
Author(s):  
Eugene Santos Jr. ◽  
Eunice E. Santos ◽  
Hien Nguyen ◽  
Long Pan ◽  
John Korah
Keyword(s):  
Information Retrieval ◽  
Real Time ◽  
Homeland Security ◽  
Large Scale ◽  
Rapid Development ◽  
Empirical Evaluation ◽  
Heterogeneous Data ◽  
Free Text ◽  
Unified Framework ◽  
Sophisticated User

With the proliferation of the Internet and rapid development of information and communication infrastructure, E-governance has become a viable option for effective deployment of government services and programs. Areas of E-governance such as Homeland security and disaster relief have to deal with vast amounts of dynamic heterogeneous data. Providing rapid real-time search capabilities for such databases/sources is a challenge. Intelligent Foraging, Gathering, and Matching (I-FGM) is an established framework developed to assist analysts to find information quickly and effectively by incrementally collecting, processing and matching information nuggets. This framework has previously been used to develop a distributed, free text information retrieval application. In this chapter, we provide a comprehensive solution for the E-GOV analyst by extending the I-FGM framework to image collections and creating a “live” version of I-FGM deployable for real-world use. We present a Content Based Image Retrieval (CBIR) technique that incrementally processes the images, extracts low-level features and map them to higher level concepts. Our empirical evaluation of the algorithm shows that our approach performs competitively compared to some existing approaches in terms of retrieving relevant images while offering the speed advantages of a distributed and incremental process, and unified framework for both text and images. We describe our production level prototype that has a sophisticated user interface which can also deal with multiple queries from multiple users. The interface provides real-time updating of the search results and provides “under the hood” details of I-FGM processes as the queries are being processed.

I-FGM as a Real Time Information Retrieval Tool for E-Governance

2011 ◽  
pp. 77-97
Author(s):  
Eugene Santos Jr. ◽  
Eunice E. Santos ◽  
Hien Nguyen ◽  
Long Pan ◽  
John Korah
Keyword(s):  
Real Time ◽  
Homeland Security ◽  
Empirical Evaluation ◽  
Heterogeneous Data ◽  
Unified Framework ◽  
Critical Areas ◽  
Unstructured Text ◽  
Real Time Information ◽  
Time Information ◽  
Incremental Process

Homeland security and disaster relief are some of the critical areas of E-governance that have to deal with vast amounts of dynamic heterogeneous data. Providing rapid real-time search capabilities for such applications is a challenge. Intelligent Foraging, Gathering, and Matching (I-FGM) is an established framework developed to assist users to find information quickly and effectively by incrementally collecting, processing and matching information nuggets. This framework has been successfully used to develop a distributed, unstructured text retrieval application. In this paper, we apply the I-FGM framework to image collections by using a concept-based image retrieval method. We approach this by incrementally processing images, extracting low-level features and mapping them to higher level concepts. Our empirical evaluation shows that our approach performs competitively compared to some existing approaches in terms of retrieving relevant images while offering the speed advantages of distributed and incremental process and unified framework between text and images.

Rapid Development of Adaptable Situation-Aware Service-based Systems

2010 ◽  
pp. 104-139 ◽  
Author(s):  
S.S. Yau ◽  
S. Mukhopadhyay ◽  
H. Davulcu ◽  
D. Huang ◽  
R. Bharadwaj ◽  
...  
Keyword(s):  
Distributed Control ◽  
Homeland Security ◽  
Situation Awareness ◽  
Service Composition ◽  
Large Scale ◽  
Rapid Development ◽  
Control Flow ◽  
Agent Platform ◽  
Service Oriented ◽  
Adaptive Workflows

Service-based systems have many applications, such as collaborative research and development, e-business, health care, military applications and homeland security. In these systems, it is necessary to provide users the capability of composing appropriate services into workflows offering higher-level functionality based on declaratively specified goals. In a large-scale and dynamic service-oriented computing environment, it is desirable that the service composition is automated and situation-aware so that robust and adaptive workflows can be generated. However, existing languages for web services are not expressive enough to model services with situation awareness (SAW) and side effects. This chapter presents an approach to rapid development of adaptable situation-aware service-based systems. This approach is based on the a-logic and a-calculus, and a declarative model for SAW. This approach consists of four major components: (1) analyzing SAW requirements using our declarative model for SAW, (2) translating the model representation to a-logic specifications and specifying a control flow graph in a-logic as the goal for situation-aware service composition., (3) automated synthesis of a-calculus terms that define situation-aware workflow agents for situation-aware service composition, and (4) compilation of a-calculus terms to executable components on an agent platform. An example of applying our framework in developing a distributed control system for intelligently and reliably managing a power grid is given.

scholarly journals Real-Time Discovery and Geospatial Visualization of Mobility and Industry Events from Large-Scale, Heterogeneous Data Streams

2016 ◽  
Author(s):  
Leonhard Hennig ◽  
Philippe Thomas ◽  
Renlong Ai ◽  
Johannes Kirschnick ◽  
He Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Large Scale ◽  
Heterogeneous Data

scholarly journals Scalable System for Smart Urban Transport Management

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Nauman Ahmad Khan ◽  
Jean-Christophe Nebel ◽  
Souheil Khaddaj ◽  
Vesna Brujic-Okretic
Keyword(s):  
Real Time ◽  
Large Scale ◽  
High Volume ◽  
Heterogeneous Data ◽  
Urban Transport ◽  
Capital City ◽  
Strategic Decision ◽  
Sensor Data ◽  
Transport Systems ◽  
Transport Management

Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city.

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

2021 ◽  
Author(s):  
Salam Ismaeel
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Power Efficiency ◽  
Large Scale ◽  
Virtual Machines ◽  
Heterogeneous Data ◽  
Energy Aware ◽  
Time Dynamic ◽  
Data Centre ◽  
Data Centres

<div>Increasing power efficiency is one of the most important operational factors for any data centre providers. In this context, one of the most useful approaches is to reduce the number of utilized Physical Machines (PMs) through optimal distribution and re-allocation of Virtual Machines (VMs) without affecting the Quality of Service (QoS). Dynamic VMs provisioning makes use of monitoring tools, historical data, prediction techniques, as well as placement algorithms to improve VMs allocation and migration. Consequently, the efficiency of the data centre energy consumption increases.</div><div>In this thesis, we propose an efficient real-time dynamic provisioning framework to reduce energy in heterogeneous data centres. This framework consists of an efficient workload preprocessing, systematic VMs clustering, a multivariate prediction, and an optimal Virtual Machine Placement (VMP) algorithm. Additionally, it takes into consideration VM and user behaviours along with the existing state of PMs. The proposed framework consists of a pipeline successive subsystems. These subsystems could be used separately or combined to improve accuracy, efficiency, and speed of workload clustering, prediction and provisioning purposes.<br></div><div>The pre-processing and clustering subsystems uses current state and historical workload data to create efficient VMs clusters. Efficient VMs clustering include less consumption resources, faster computing and improved accuracy. A modified multivariate Extreme Learning Machine (ELM)-based predictor is used to forecast the number of VMs in each cluster for the subsequent period. The prediction subsystem takes users’ behaviour into consideration to exclude unpredictable VMs requests.<br></div><div>The placement subsystem is a multi-objective placement algorithm based on a novel Machine Condition Index (MCI). MCI represents a group of weighted components that is inclusive of data centre network, PMs, storage, power system and facilities used in any data centre. In this study it will be used to measure the extent to which PM is deemed suitable for handling the new and/or consolidated VM in large scale heterogeneous data centres. It is an efficient tool for comparing server energy consumption used to augment the efficiency and manageability of data centre resources.</div><div> The proposed framework components separately are tested and evaluated with both synthetic and realistic data traces. Simulation results show that proposed subsystems can achieve efficient results as compared to existing algorithms. <br></div>

Scalable Estimator for Multi-task Gaussian Graphical Models Based in an IoT Network

2021 ◽  
Vol 17 (3) ◽  
pp. 1-33
Author(s):  
Beilun Wang ◽  
Jiaqi Zhang ◽  
Yan Zhang ◽  
Meng Wang ◽  
Sen Wang
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Graphical Model ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
Joint Estimation ◽  
Gaussian Graphical Models ◽  
Novel Approach ◽  
Cloud Server ◽  
Iot Devices

Recently, the Internet of Things (IoT) receives significant interest due to its rapid development. But IoT applications still face two challenges: heterogeneity and large scale of IoT data. Therefore, how to efficiently integrate and process these complicated data becomes an essential problem. In this article, we focus on the problem that analyzing variable dependencies of data collected from different edge devices in the IoT network. Because data from different devices are heterogeneous and the variable dependencies can be characterized into a graphical model, we can focus on the problem that jointly estimating multiple, high-dimensional, and sparse Gaussian Graphical Models for many related tasks (edge devices). This is an important goal in many fields. Many IoT networks have collected massive multi-task data and require the analysis of heterogeneous data in many scenarios. Past works on the joint estimation are non-distributed and involve computationally expensive and complex non-smooth optimizations. To address these problems, we propose a novel approach: Multi-FST. Multi-FST can be efficiently implemented on a cloud-server-based IoT network. The cloud server has a low computational load and IoT devices use asynchronous communication with the server, leading to efficiency. Multi-FST shows significant improvement, over baselines, when tested on various datasets.

scholarly journals Analysis on an Auto Increment Detection System of Chinese Disaster Weibo Text

2021 ◽  
Vol 27 (2) ◽  
pp. 230-252
Author(s):  
Hua Bai ◽  
Hualong Yu ◽  
Guang Yu ◽  
Alvaro Rocha ◽  
Xing Huang
Keyword(s):  
Social Media ◽  
Emergency Management ◽  
Real Time ◽  
Large Scale ◽  
Rapid Development ◽  
Vector Model ◽  
Collection System ◽  
Single Text ◽  
Text Feature ◽  
Disaster Situation

With the rapid development of Internet information technology, the advantages of social media in terms of speed, content, form, and effect of communication are becoming increasingly significant. In recent years, more and more researchers have paid attention to the special value and role of social media tools in disaster information emergency management. Weibo is the most widely used Chinese social media tool. To effectively mine and apply the emergency function of disaster situation microblogs, a disaster situation information discovery and collection system capable of online incremental identification and collection are constructed for massive and disordered disaster microblog text streams. First, based on the deep learning- trained word vector model and a large-scale corpus, an unsupervised short-text feature representation method of disaster situation Weibo information is developed. According to the experimental results of the feature combination test and the training set scale test, the SVM algorithm was selected for disaster microblog information classification, which realized effective identification of disaster situation micro-bloggings. Then, the temporal information similarity and geographic information similarity are used to improve the single text similarity algorithm, and a Chinese disaster event online real-time detection model is constructed. Furthermore, the disaster-affected areas can be achieved in real-time based on the detection results. By crawling and classifying the micro-bloggings from the disaster-affected areas, it is possible to realize the incremental identification and collection of online disaster situation Weibo information. Finally, the empirical analysis of disaster events such as the &ldquo;Leshan Earthquake&rdquo; shows that the real- time intelligent identification and collection system for disaster situation Weibo micro-bloggings developed in this paper can obtain large-scale and useful data for disaster emergency management, which proving that this system is effective and efficient.

An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

2021 ◽  
Author(s):  
Salam Ismaeel
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Power Efficiency ◽  
Large Scale ◽  
Virtual Machines ◽  
Heterogeneous Data ◽  
Energy Aware ◽  
Time Dynamic ◽  
Data Centre ◽  
Data Centres

<div>Increasing power efficiency is one of the most important operational factors for any data centre providers. In this context, one of the most useful approaches is to reduce the number of utilized Physical Machines (PMs) through optimal distribution and re-allocation of Virtual Machines (VMs) without affecting the Quality of Service (QoS). Dynamic VMs provisioning makes use of monitoring tools, historical data, prediction techniques, as well as placement algorithms to improve VMs allocation and migration. Consequently, the efficiency of the data centre energy consumption increases.</div><div>In this thesis, we propose an efficient real-time dynamic provisioning framework to reduce energy in heterogeneous data centres. This framework consists of an efficient workload preprocessing, systematic VMs clustering, a multivariate prediction, and an optimal Virtual Machine Placement (VMP) algorithm. Additionally, it takes into consideration VM and user behaviours along with the existing state of PMs. The proposed framework consists of a pipeline successive subsystems. These subsystems could be used separately or combined to improve accuracy, efficiency, and speed of workload clustering, prediction and provisioning purposes.<br></div><div>The pre-processing and clustering subsystems uses current state and historical workload data to create efficient VMs clusters. Efficient VMs clustering include less consumption resources, faster computing and improved accuracy. A modified multivariate Extreme Learning Machine (ELM)-based predictor is used to forecast the number of VMs in each cluster for the subsequent period. The prediction subsystem takes users’ behaviour into consideration to exclude unpredictable VMs requests.<br></div><div>The placement subsystem is a multi-objective placement algorithm based on a novel Machine Condition Index (MCI). MCI represents a group of weighted components that is inclusive of data centre network, PMs, storage, power system and facilities used in any data centre. In this study it will be used to measure the extent to which PM is deemed suitable for handling the new and/or consolidated VM in large scale heterogeneous data centres. It is an efficient tool for comparing server energy consumption used to augment the efficiency and manageability of data centre resources.</div><div> The proposed framework components separately are tested and evaluated with both synthetic and realistic data traces. Simulation results show that proposed subsystems can achieve efficient results as compared to existing algorithms. <br></div>

Cross Language Query Expansion Approach for CIMS Based on Weighted D-S Evidence Theory

2014 ◽  
Vol 620 ◽  
pp. 534-543
Author(s):  
Xiao Bo Wang ◽  
Fan Zhao ◽  
Xiao Li ◽  
Rong Hui Zhang
Keyword(s):  
Information Technology ◽  
Information Retrieval ◽  
Large Scale ◽  
Query Expansion ◽  
Semantic Analysis ◽  
Rapid Development ◽  
Evidence Theory ◽  
Computer Integrated Manufacturing ◽  
Shafer Theory ◽  
Cross Language

With the Computer Integrated Manufacturing System and Information Technology rapid development, rapid retrieval multilingual becomes one of the hot spots in Machine Translation. The cross-language information retrieval (CLIR) provides a convenient way, enabling users to use their own familiar language to submit queries to retrieve documents in another language. Basic query expansion is one of the effective methods to improve recall of information retrieval. There are many researchers have proposed many extension methods, but most methods are simply added to the query expansion terms. If we do not distinguish the original query words and extended words, expanded query may deviate from the original semantics. So, it is very inconvenience for mechanical engineer and programmer. Based on Dempster-Shafer theory of evidence, we proposed a query expansion computing model, which considered as the main evidence of the original query terms, while the extensions as a secondary evidence of the original query terms. Which method to use semantic dictionary Han and Uygur-Chinese bilingual dictionary of synonyms forest and How to get the query word synonyms, near-synonyms and hypernym. Latent Semantic Analysis is used to obtain semantic relationships query words related words the using potentially large-scale text. The combination of these two types of evidence is in order to put forward a weighted combination of the Dempster-Shafer rule. Experimental results show that this method can effectively improve retrieval efficiency in Mechanical Engineering and Information Technology. The research results can be provided a reference for CIMS multilingual quick retrieval.

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

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