Data Management Layer Research Articles

Hazards Detection System Based on 3D Panorama for Tailings Dam in Mining

Tailings dam is a key facility for the mining process, which is also the major hazards for the surrounding environments. It could cause catastrophic accidents and severe personal injuries if the tailings dam failed. The occurrence of tailings dam accidents will greatly damage the life and property of the people in the surrounding areas and damage the ecological environment around the region. In recent years, Chinese government has introduced policies to step up investigation and management of mine tailings potential hazards and results in great achievements. However, a large part of the tailings dam still has prominent safety problems due to long-term site selection, design, system construction and management. Flood drainage system plays an important role both in the mining process and in the tailings dam safety. Through the analysis of the causes of tailings dam accidents, this paper discoveries that a number of accidents caused by the failure or damage of flood drainage facilities, and the risk in flood season is especially prominent. The safety hazards detection of the flood drainage is vital to secure the tailings dam safety. A safety hazards detection system based on 3D panorama technology has been achieved in this paper. It provided a new way to detect the safety hazards remotely on portable devices. Firstly, the 3D panorama data acquisition device was designed and developed. The functions and requirements of 3D panorama data acquisition device were investigated according to the actual environment of flood culvert. The structure and hardware of the device were calculated according to the target functions. Three-dimensional environment image data for rapid acquisition, and the use of feature matching and optical flow principle of the collected images were spliced to generate a 3D panoramic image of flood drainage of tailings dam in mining. The safety hazards detection system includes 3D panorama client and panorama images cloud database. It has five layers which are basic configuration layer, interface presentation layer, data management layer, function module layer and background management layer. The functional module layer has five functional modules, including mine personnel management module, tailings pond panoramic scene module, culvert potential danger detection module, interface menu navigation module and material information management module. It can help safety inspectors to detect and investigate potential safety hazards in flood drainage culverts, which would strengthen the safe operation of tailings dam and maintain the safe and sustainable production of mines. This system has been applied to the tailings dam of a mine to collect 3D environmental image data of the flood drainage culvert. The panoramic image is spliced to generate a panoramic view through the cloud platform. The results show that this system can be used in mine drainage culvert safety hazards detection.

A Real-time Cyber-Physical System for Indoor Environment Monitoring

Different from the traditional embedded system or the real-time system, Cyber-Physical System (CPS) integrates computation with physical entities, making it easy for people to interact with the world that we live. With the development of CPS, more devices will be integrated into the internet, especially in the field of Environment Monitoring. Thus, the management and remote control of devices cause issues for CPSs. Towards this end, we develop a CPS by proposing a scalable, flexible architecture in Environment monitoring. The environment monitoring system (EMS) consists of three layer: physical sensing layer, data transport layer, data management layer. Generally, sensors and sensor hub which collects sensor data and transfer them to the upper layer make up of physical sensing layer. And the main function for data transport layer is to forward sensor data to the specified destination. The last layer is in the cloud, composed of a data processor and a knowledge base. Data from physical sensing layer can be analyzed due to the strong computation ability of cloud computing. Also, there are abundant restful API (application programming interfaces) are provided by management layer based on users’ demand. The proposed architecture provide convenient management with devices since all the information of devices are stored into database in the cloud. Even under the poor condition of network, data produced by sensors can be saved in local storage, making it enough time for user to settle the network problem with no affecting on the environment monitoring.

Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data

The advances in information technology have witnessed great progress on healthcare technologies in various domains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the characteristics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technologies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.

PSUP: A Planetary SUrface Portal

The large size and complexity of planetary data acquired by spacecraft during the last two decades create a demand within the planetary community for access to the archives of raw and high level data and for the tools necessary to analyze these data. Among the different targets of the Solar System, Mars is unique as the combined datasets from the Viking, Mars Global Surveyor, Mars Odyssey, Mars Express and Mars Reconnaissance Orbiter missions provide a tremendous wealth of information that can be used to study the surface of Mars. The number and the size of the datasets require an information system to process, manage and distribute data. The Observatories of Paris Sud (OSUPS) and Lyon (OSUL) have developed a portal, called PSUP (Planetary SUrface Portal), for providing users with efficient and easy access to data products dedicated to the Martian surface. The objectives of the portal are: 1) to allow processing and downloading of data via a specific application called MarsSI (Martian surface data processing Information System); 2) to provide the visualization and merging of high level (image, spectral, and topographic) products and catalogs via a web-based user interface (MarsVisu), and 3) to distribute some of these specific high level data with an emphasis on products issued by the science teams of OSUPS and OSUL. As the MarsSI service is extensively described in a companion paper (Quantin-Nataf et al., companion paper, submitted to this special issue), the present paper focus on the general architecture and the functionalities of the web-based user interface MarsVisu. This service provides access to many data products for Mars: albedo, mineral and thermal inertia global maps from spectrometers; mosaics from imagers; image footprints and rasters from the MarsSI tool; high level specific products (defined as catalogs or vectors). MarsVisu can be used to quickly assess the visualized processed data and maps as well as identify areas that have not been mapped yet. It also allows overlapping of these data products on a virtual Martian globe, which can be difficult to use collectively. The architecture of PSUP data management layer and visualization is based on SITools2 (Malapert and Marseille, 2012) and MIZAR (Module for Interactive visualiZation from Astronomical Repositories) respectively, two CNES generic tools developed by a joint effort between the French space agency (CNES) and French scientific laboratories. Future developments include the addition of high level products of Mars (regional geological maps, new global compositional maps…) and tools (spectra extraction from hyperspectral cubes). Ultimately, PSUP will be adapted to other planetary surfaces and space missions in which the French research institutes are involved.

An IoT-Aware Architecture for Collecting and Managing Data Related to Elderly Behavior

The world population will be made up of a growing number of elderly people in the near future. Aged people are characterized by some physical and cognitive diseases, like mild cognitive impairment (MCI) and frailty, that, if not timely diagnosed, could turn into more severe diseases, like Alzheimer disease, thus implying high costs for treatments and cares. Information and Communication Technologies (ICTs) enabling the Internet of Things (IoT) can be adopted to create frameworks for monitoring elderly behavior which, alongside normal clinical procedures, can help geriatricians to early detect behavioral changes related to such pathologies and to provide customized interventions. As part of the City4Age project, this work describes a novel approach for collecting and managing data about elderly behavior during their normal activities. The data capturing layer is an unobtrusive and low-cost sensing infrastructure abstracting the heterogeneity of physical devices, while the data management layer easily manages the huge quantity of sensed data, giving them semantic meaning and fostering data shareability. This work provides a functional validation of the proposed architecture and introduces how the data it manages can be used by the whole City4Age platform to early identify risks related to MCI/frailty and promptly intervene.

An Expert System to Assist the Diagnosis of Ischemic Heart Disease

Motivations: Ischemic Heart Disease (IHD) is one of the most common causes of the death in many countries [1]. The disease is unpredictable in onset and hence, requires rigorous diagnosis in regards to multiple signs and symptoms such as cardiac pain, breathlessness, sweating, palpitation, nausea vomiting and hypertension [2]. However, these signs and symptoms cannot be measured with full confidence because of the presence of various uncertainties such as vagueness, imprecision, ambiguity, ignorance and incompleteness [3]. Therefore, traditional diagnosis by the physician lacks the reliability in two ways: a) uncertainty issues in reading the signs and symptoms, and b) difficulty to handle multiple signs and symptoms simultaneously as a human agent. Fuzzy expert systems can be considered for IHD diagnosis; however they are not able to address all types of uncertainty, especially ignorance, incompleteness and ignorance in fuzziness [4]. Such uncertainties were addressed by using a Belief Rule Base (BRB) in the literature in assessing clinical asthma suspicion [3]. In this paper, we propose a Belief Rule Based Expert System (BRBES) to assist the physician to handle all kinds of uncertainty that exist with the signs and symptoms of IHD.Method: The proposed expert system uses BRB to develop the knowledge base and Evidential Reasoning (ER) as inference methodology in an integrated system, named as RIMER [5]. RIMER is a relatively new knowledge representation and inferencing scheme that can handle various types of uncertainties such as vagueness, ambiguity, imprecision, ignorance and incompleteness. Therefore, RIMER allows the handling of various types of uncertainty exist with the signs and symptoms of IHD. The architecture of the proposed BRBES is module based while its layer based user friendly interface allows capturing of input data from the signs and symptoms of IHD as well as allows the assessment of the IHD level of a patient. The whole system has been implemented as a desktop application. The backend data management layer has been constructed using Microsoft SQL server to handle the knowledge base. The interface engine has been developed by using C#.NET.Results and Discussion: A data set of 200 patients has been used to generate results from the BRBES. These have been compared with the results generated from Fuzzy based expert system (FBES) as well as with a manual system where a physician gives the assessment of the IHD level. The Area under Curve (AUC) parameter (the larger the better) of Recover Operating Characteristics (ROC) curves have been used to compare the reliability of the BRBES with FBES and the manual system. The AUC of BRBES is found as 0.949 (95% confidence intervals 0.729 - 0.970) while the AUC of Manual System is 0.811 (95% confidence intervals 0.675 - 0.947) and the AUC of FBES is 0.884 (95% confidence intervals 0.693 - 0.956). The reliability of manual approach is less because the physicians is unable to deliver desired accuracy in assessing IHD due to in most of the cases their mindset of diagnosis is Boolean as observed during our research. It can also be observed from the presented data that the AUC of BRBES is greater than that of FBES because the later considers uncertainties due to vagueness, imprecision and ambiguity while earlier in addition to these uncertainties considers uncertainty due to randomness and ignorance. The proposed system is intended for assisting the physicians in health checkup as an assisting monitoring technology [6].

Designing Technology-Enabled Instruction to Utilize Learning Analytics

A key notion conveyed by those who advocate for the use of data to enhance instruction is an awareness that learning analytics has the potential to improve instruction and learning but is not currently reaching that potential. Gibbons (2014) suggested that a lack of learning facilitated by current technology-enabled instructional systems may be due in part to the natural tendency of many designers to focus on the surface layers of instruction (i.e., the content and control layers), while failing to adequately design the internal (less visible) aspects (e.g., the data management layer). In this paper, we outline phases in the design process related to the data management layer that should be considered when integrating learning analytics into a technology-enabled learning system.

Integration of EBSCO Discovery Service widget into the learning spaces of LMS

PurposeRecent advancement in technological development has encouraged distance learning institutions to be more productive and creative in effectively utilizing the Learning Management System (LMS). Among the many measures employed is the integration of federated search engine into the LMS which allows for a more productive and wider scope of information retrieval through the provisions of library resources and services. The purpose of this paper is to report one such case study in Wawasan Open University exploring the integration of federated search engine (EBSCO Discovery Service (EDS) widget) into the learning spaces of LMS. Widgets resemble apps that enable the integration of EDS functionality in providing access for students to retrieve library learning resources from the convenience of the LMS, excluding the need to log onto the library.Design/methodology/approachThis paper presents a discussion that highlights the development and conjectural implementation of a framework on the integration of the EDS widget into the University’s LMS. Data collection includes meta-analysis data from the micro- and macro-level infrastructure that make up the framework, namely, end-user layer, system layer and data management layer.FindingsFindings from this study addressed significant importance to the library in promoting effective search and utilization of information needs. The findings will also make clear recommendations in developing effective collaborations between the library and faculties. Although the implementation of this framework is still in a developmental stage, this study still provides pertinent information in validating the integration of EDS into the University’s LMS.Research limitations/implicationsWhile serious limitations are not anticipated, possible concerns do exist with programming algorithms in the integration of EDS into the LMS. These challenges will be reported in the paper as reference for future replications of studyPractical implicationsOne key implication is the increase in the usage of the library resources and the potential to reach a larger audience of remote library users.Originality/valueThe primary advantage is to minimize the need for multiple gateway login while ensuring the library to monitor relevant library databases activities throughout the system check of the LMS.

Achieving High Performance on Supercomputers with a Sequential Task-based Programming Model

The emergence of accelerators as standard computing resources on supercomputers and the subsequent architectural complexity increase revived the need for high-level parallel programming paradigms. Sequential task-based programming model has been shown to efficiently meet this challenge on a single multicore node possibly enhanced with accelerators, which motivated its support in the OpenMP 4.0 standard. In this paper, we show that this paradigm can also be employed to achieve high performance on modern supercomputers composed of multiple such nodes, with extremely limited changes in the user code. To prove this claim, we have extended the StarPU runtime system with an advanced inter-node data management layer that supports this model by posting communications automatically. We illustrate our discussion with the task- based tile Cholesky algorithm that we implemented on top of this new runtime system layer. We show that it allows for very high productivity while achieving a performance competitive with both the pure Message Passing Interface (MPI)-based ScaLAPACK Cholesky reference implemen- tation and the DPLASMA Cholesky code, which implements another (non sequential) task-based programming paradigm.

Health Care Virtual Environment System Reinforced by Hadoop and Cloud

The advances in information technology have wit- nessed great progress on healthcare technologies in various do- mains nowadays. However, these new technologies have also made healthcare data not only much bigger but also much more difficult to handle and process. Moreover, because the data are created from a variety of devices within a short time span, the character- istics of these data are that they are stored in different formats and created quickly, which can, to a large extent, be regarded as a big data problem. To provide a more convenient service and environment of healthcare, this paper proposes a cyber-physical system for patient-centric healthcare applications and services, called Health-CPS, built on cloud and big data analytics technolo- gies. This system consists of a data collection layer with a unified standard, a data management layer for distributed storage and parallel computing, and a data-oriented service layer. The results of this study show that the technologies of cloud and big data can be used to enhance the performance of the healthcare system so that humans can then enjoy various smart healthcare applications and services.

Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures

The availability of powerful, worldwide span computing facilities offering application scalability by means of cloud infrastructures perfectly matches the needs for resources that characterize Big Data applications. Elasticity of resources in the cloud enables application providers to achieve results in terms of complexity, performance and availability that were considered beyond affordability, by means of proper resource management techniques and a savvy design of the underlying architecture and of communication facilities. This paper presents an evaluation technique for the combined effects of cloud elasticity and Big Data oriented data management layer on global scale cloud applications, by modeling the behavior of both typical in memory and in storage data management.

Control-Theoretic Methods for Cyberphysical Security: Geometric Principles for Optimal Cross-Layer Resilient Control Systems

Cyberphysical systems integrate physical processes, computational resources, and communication capabilities. Cyberphysical systems have permeated modern society, becoming prevalent in many domains, including energy production, health care, and telecommunications. Examples of cyberphysical systems include sensor networks, industrial automation systems, and critical infrastructures such as transportation networks, power generation and distribution networks, water and gas distribution networks, and advanced manufacturing systems. The integration of cybertechnologies with physical processes increases system efficiencies and, at the same time, introduces vulnerabilities that undermine the reliability of critical infrastructures. As recently highlighted by the Maroochy water breach in March 2000 [1], multiple recent power blackouts in Brazil [2], the SQL Slammer worm attack on the Davis-Besse nuclear plant in January 2003 [3], the StuxNet computer worm in June 2010 [4], and various industrial security incidents [5], cyberphysical systems are prone to failures and attacks on their physical infrastructure and cyberattacks on their data management and communication layer [6], [7].

The Design and Implementation of the Information Remote Monitoring and Security Management System Based on Internet

With the development of economy, the existence and application of the inflammable, explosive, toxic and harmful gases irresistible but dangerous at the same time in the industrial production application. for the sake of minimizing potential dangers, the remote security monitoring is of great significance. With the rapid development of network technology, the Internet network technology to implement the remote monitoring can effectively avoid some potential hazards in the industrial production and other dangerous things. We can draw a conclusion from the feedback that the effect of user experience is not ideal in the actual application. So based on the analysis of the traditional structure of remote monitoring system, a new remote monitoring system is created which can avoid these shortcomings through further research. This new system consists of three parts: 1. The client: using Flex rich client technology is more convenient and suitable for communication interface and let the customer feel familiar and easy to use. 2. data management layer: it contains three parts : the Web server layer, application server and database server layer . Application server promotes system real-time performance through Web Service technology, Shared memory and streaming Socket technology, which enables clients to have direct access to the site. 3. Data acquisition layer: data will be collected in a timely manner to send to data management with GPRS technology.

An Architecture for Unstructured Data Management

As the information age is coming, there is a vast amount of information available in the Internet. Most of data on Web are unstructured. But the significant data should be organized and stored in a suitable way for future purposes. One of the unsolved problems is the management of unstructured data. The unstructured data such as presentation, spreadsheet, text document, memo, images and web pages are difficult to manage while the data become a large scale and the users have different requirements and interests. In this paper, we proposed an architecture for unstructured data management by integrating source query, data collection and data management to solve these problems. The data collection layer extracts the data we care about, we use the existing tools to extract automatic and we can also add the data to the repository manually. The data management layer manage all the collection data by classifying the data, selecting nodes to store and managing centralized as index. The source query layer allows users to query and get the data diversity according the adaptive query service and recommendation service. Finally, we implemented a prototype system OCourse based on this system architecture to show its feasible and efficient.

A Data Storage and Management Scheme in Cloud Storage Model

The cloud storage is the evolution of the cloud computing system, which is based on data storage and management. This thesis is focused on the discussion of the cloud storage model that is built on the Cloud Storage Access Protocol of SNIA, and puts forward the concept of storage pooling at storage management layer with unified virtualization and dynamic management on physical storage devices. The thesis also compares the typical DFS (Distributed File System) on the data management layer in different data conditions.

A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE

Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

NEIMiner: nanomaterial environmental impact data miner.

As more engineered nanomaterials (eNM) are developed for a wide range of applications, it is crucial to minimize any unintended environmental impacts resulting from the application of eNM. To realize this vision, industry and policymakers must base risk management decisions on sound scientific information about the environmental fate of eNM, their availability to receptor organisms (eg, uptake), and any resultant biological effects (eg, toxicity). To address this critical need, we developed a model-driven, data mining system called NEIMiner, to study nanomaterial environmental impact (NEI). NEIMiner consists of four components: NEI modeling framework, data integration, data management and access, and model building. The NEI modeling framework defines the scope of NEI modeling and the strategy of integrating NEI models to form a layered, comprehensive predictability. The data integration layer brings together heterogeneous data sources related to NEI via automatic web services and web scraping technologies. The data management and access layer reuses and extends a popular content management system (CMS), Drupal, and consists of modules that model the complex data structure for NEI-related bibliography and characterization data. The model building layer provides an advanced analysis capability for NEI data. Together, these components provide significant value to the process of aggregating and analyzing large-scale distributed NEI data. A prototype of the NEIMiner system is available at http://neiminer.i-a-i.com/.

A Framework of Smart Supermarket Based on the Internet of Things

In this paper, we propose a framework of smart supermarket using Internet of Things, which includes three layers: perception layer, data management layer and application layer. This framework is quite powerful, flexible and extensible for constructing smart supermarkets. The key technological issues for this framework are also discussed.

Efficient data management for incoherent ray tracing

To obtain good performance on the GPU hardware, it is necessary to design algorithms to manage data, access memory under GPU memory hierarchy, and schedule more efficient threads. In this paper, we propose an efficient data management and task management designed for GPU based ray tracing. Due to the dynamic and uncertainty in ray tracing, we design data-management layer and task-management layer combined with fuzzy spatial analysis, use the two-level ray sorting and a ray bucket structure to reorganize ray data, then a warp's threads can be scheduled to access coherent geometry and nodes data, reduce memory bandwidth, and dispatch the data locally. We schedule tasks in data-driven execution according to coherent data, propose an adaptive ray compaction to eliminate inactive threads, maintain task efficiency of threads in a warp, and design two heuristics to decrease the compaction cost. On the basis of it, we also introduce a memory-optimized dynamic traversal management to reduce incoherent memory access, and avoid frequent sorting computation and compaction operations. Our experiments demonstrate all of these work combined can achieve good performance.

A context management framework for context‐aware applications in mobile spaces

PurposeThe rapid proliferation of mobile context aware applications has resulted in an increased research interest towards developing specialized context data management strategies for mobile entities. The purpose of this paper is to aim to develop a new way to model mobile entities and manage their contexts accordingly.Design/methodology/approachThis paper proposes the concept of “Mobile Space” to model mobile entities and presents strategies to manage the various contexts associated therein. To handle availability related issues, two system services are designed: the “Availability Updating Service” which is an identifier based mechanism and is designed to keep track of mobile objects and handle availability related issues, and the “Application Callback Service” which is a publish/subscribe based mechanism to handle application disruptions and interruptions arising due to mobility.FindingsThe paper presents a detailed study of the proposed framework and a description of the underlying services and the components therein to validate the framework. Experimental results carried out in WiFi and 3G environments indicate that the proposed techniques can support mobile applications and minimize application disruptions with minimal overhead.Originality/valueThe proposed context management framework is generic in nature and is not designed for a specific class of applications. Any mobile context aware application can leverage on the framework and utilize the provided functionalities to manage application disruptions. Also, the decoupling of mobile application layer and the underlying context data management layer renders context data management layer transparent to the application design.