Effective Utilization Of Data Research Articles

Intelligent context-based healthcare metadata aggregator in internet of medical things platform

Abstract The internet of medical things (IoMT) is relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits in terms of smart future network computing and intelligent health-care systems. Effective utilization of the health-care data is the key factor here in achieving such potential, which can be a significant challenge as the data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority to access it. To address this issue, in this paper, we introduce an intelligent context-based metadata aggregator in the decentralized and distributed edge-based IoMT platform with a use case of early sepsis detection using clinical data. We thoroughly discuss the various aspects of the metadata aggregator and the overall IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications.

Achieving Secure and Dynamic Range Queries Over Encrypted Cloud Data

Cloud computing is motivating data owners to outsource their databases to the cloud. However, for privacy concerns, the sensitive data has to be encrypted before outsourcing, which inevitably posts a challenging task for effective data utilization. Existing work either focuses on keyword searches, or suffers from inadequate security guarantees or inefficiency. In this paper, we concentrate on multi-dimensional range queries over dynamic encrypted cloud data. We first propose a tree-based private range query scheme over dynamic encrypted cloud data (TRQED), which supports faster-than-linear range queries and protects single-dimensional privacy. Then, we discuss the defects of TRQED in terms of privacy-preservation. We modify the framework of the system by adopting a two-server model and put forward a safer range query scheme, called TRQED $^+$ . By newly designed secure node query (SNQ) and secure point query (SPQ), we propose the perturbation-based oblivious R-tree traversal (ORT) operation to preserve both path pattern and stronger single-dimensional privacy. Finally, we conduct comprehensive experiments on real-world datasets and perform comparisons with existing works to evaluate the performance of the proposed schemes. Experimental results show that our TRQED and TRQED $^+$ surpass the state-of-the-art methods in privacy-preservation level and efficiency.

Assessment of Adolescent and Youth Friendly Health Services in Sri Lanka using WHO’s ‘4S’ Framework

Abstract Background The Adolescent and Youth Friendly Health Services (AYFHS) in Sri Lanka (SL) are provided via three service models, viz., hospital-based, field-based and centre-based. This study assessed the implementation status of AYFHS in SL. Methods A panel of experts conducted the review by adopting a participatory approach. Policies and supporting documents were collected from the relevant sectors of the Ministry of Health using electronic and manual search and by visiting institutions/resource persons. The strengths and weaknesses of the AYFHS were identified using the WHO’s ’4S’ Framework consisting strategic information (S1), supportive evidence-informed policies (S2), strengthening services for adolescents (S3) and strengthening collaboration with other sectors (S4). Results While recent development of guidelines, standards and protocols in line with WHO/UNAIDS ’Global Standards for Quality Health-care Services for Adolescents’ was the main strength on S1, gaps in timeliness, completeness and accuracy of the data; and the gaps in effective utilization of sub-national level data addressing area specific problems were the main weaknesses. On S2, SL has a strong, supportive policy environment to promote adolescent health. Lack of coherence in existing health related policies was the main gap. A comprehensive health care package was a strength on S3, while difficulty in reaching the vulnerable groups, curative service oriented service package and underutilization of the health services by adolescents were the weaknesses. On S4, lack of sustained political commitment identifying adolescent health as a priority development issue across multi-stakeholders and their silo mentality to maintain the accountability/ownership of cross-cutting programmatic activities were the weaknesses. Conclusions Despite numerous imposing challenges, with mainstreaming and constructive monitoring, the AYFHS in SL will remain as one of the best adolescent health programmes in the region. Key messages Assessment of Sri Lankan AYFHS according to the WHO’s ’4S’ Framework revealed multiple strengths such as updated guidelines, supportive policy environment and comprehensive care package. Despite numerous imposing challenges, with mainstreaming and constructive monitoring, the AYFHS in SL will remain as one of the best adolescent health programmes in the region.

Research on Power Data Management Based on Sovereign Blockchain Technology

The development of smart grid technology has improved the way in which grid companies interact with power users, but there are data breaches when data falls into the hands of malicious attackers. This paper implements a multi-blockchain system based on sovereign blockchain technology for the main body of the power trading market, and designs the data transmission mechanism and system architecture. Through the power data classification index, the effective mining and utilization of intelligent power data is realized. Incorporating national sovereignty constraints into smart contracts ensures that smart grids provide a system of trusted, nationally compliant systems for power users’ electricity transactions. Finally, the advantages of the power data management scheme proposed in this paper are analysed by comparing with the traditional power data management methods.

Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework.

ObjectivePrescription medication (PM) misuse and abuse is a major health problem globally, and a number of recent studies have focused on exploring social media as a resource for monitoring nonmedical PM use. Our objectives are to present a methodological review of social media–based PM abuse or misuse monitoring studies, and to propose a potential generalizable, data-centric processing pipeline for the curation of data from this resource.Materials and MethodsWe identified studies involving social media, PMs, and misuse or abuse (inclusion criteria) from Medline, Embase, Scopus, Web of Science, and Google Scholar. We categorized studies based on multiple characteristics including but not limited to data size; social media source(s); medications studied; and primary objectives, methods, and findings.ResultsA total of 39 studies met our inclusion criteria, with 31 (∼79.5%) published since 2015. Twitter has been the most popular resource, with Reddit and Instagram gaining popularity recently. Early studies focused mostly on manual, qualitative analyses, with a growing trend toward the use of data-centric methods involving natural language processing and machine learning.DiscussionThere is a paucity of standardized, data-centric frameworks for curating social media data for task-specific analyses and near real-time surveillance of nonmedical PM use. Many existing studies do not quantify human agreements for manual annotation tasks or take into account the presence of noise in data.ConclusionThe development of reproducible and standardized data-centric frameworks that build on the current state-of-the-art methods in data and text mining may enable effective utilization of social media data for understanding and monitoring nonmedical PM use.

Estimating global gross primary productivity using chlorophyll fluorescence and a data assimilation system with the BETHY-SCOPE model

Abstract. This paper presents the assimilation of solar-induced chlorophyll fluorescence (SIF) into a terrestrial biosphere model to estimate the gross uptake of carbon through photosynthesis (GPP). We use the BETHY-SCOPE model to simulate both GPP and SIF using a process-based formulation, going beyond a simple linear scaling between the two. We then use satellite SIF data from the Orbiting Carbon Observatory-2 (OCO-2) for 2015 in the data assimilation system to constrain model biophysical parameters and GPP. The assimilation results in considerable improvement in the fit between model and observed SIF, despite a limited capability to fit regions with large seasonal variability in SIF. The SIF assimilation increases global GPP by 31 % to 167±5 Pg C yr−1 and shows an improvement in the global distribution of productivity relative to independent estimates, but a large difference in magnitude. This change in global GPP is driven by an overall increase in photosynthetic light-use efficiency across almost all biomes and more minor, regionally distinct changes in APAR. This process-based data assimilation opens up new pathways to the effective utilization of satellite SIF data to improve our understanding of the global carbon cycle.

Positioning Error in Mobile Phone Tracking Data with Consideration of Geographic Environment Factors

Abstract. Summary. This research contributes to both theory and application. This is an important supplement to the research on the quality and uncertainty of spatial big data and it will help mobile phone data play a more effective application value in future research. In recent years, the rapid development of data acquisition means has led to the rapid accumulation of spatial data and generated spatial big data with geographic location information. However, the uncertainty in spatial big data leads to the quality problem of spatial big data, which is one of the main bottlenecks restricting the effective utilization of spatial big data. As an important part of spatial big data, it is necessary to study the quality and uncertainty of mobile phone data. In view of this, we delve into the key geographical environment factors that affect the positioning error of mobile phone data, extract error evaluation indexes and construct the spatial distribution model of positioning error with machine learning algorithms. To explore the distribution law of mobile phone data positioning error under different geographical scenarios. In addition, considering the high accuracy of personal travel GPS positioning, this study takes personal travel GPS positioning data as the comparative object of mobile phone data for analysis.

Evaluation of heterogeneous uncertain information fusion

In this paper our motivation is to provide evaluation approaches for information fusion where both possibilistic uncertainty and probabilistic uncertainty occurs. For effective utilization of such diverse data, fusion is used to assist decision-making. However it is necessary to evaluate the results in order to assess their value. Our innovation is the use of information and specificity measures to provide assessments of the fusion results. In particular we investigate transformation-based approaches to the problem of combining possibility and probability distributions. We consider a total heterogeneous fusion process as having three phases in general. Phase 1 is a transformation phase used to produce homogenous data representations. Specifically we explore two transformations—probability to possibility and vice versa. Phase 2 consists of specific aggregation functions operating on the homogenous formatted data. For aggregation functions we representatively cover a range of possible functions by using min, max and average. Phase 3 consists of the applicable assessment measures on the fusion results. Two examples of the complete approach for representative probability and possibility distributions are worked out in full detail and evaluation techniques are used to compare the various results. Finally, general evaluative comparisons of the approaches are given based on extreme bounding cases of completely certain and uncertain probability and possibility distributions. Our contribution then has been to provide approaches to understand which aggregation functions from the min, avg, max spectrum and which transformations would be most useful for the fusion result.

Demographic Data Analysis And Educational System Planning For Primary Education Delivery In Abia State, Nigeria.

The determination of the level of demographic analysis and educational system planning in the provision of primary education in Abia State, Nigeria was the thrust of this study. The study adopted the descriptive survey design. All the senior staff of the Ministry of Education and the Universal Basic Education Board constituted the population and the respondents. They were 59 in number. The instrument for the generation of data were questionnaire and structured interview. The title of the questionnaire was Demographic Data Analysis and Education System Planning for the provision of Primary Education Questionnaire (DDAESPPPEQ). The items of the instrument were structured based on the four point modified Likert response mode. The data were analyzed with Mean, standard deviation and t-test of difference. The findings of the study include the following: inadequate policy provisions and administrative strategies to ensure effective demographic data collection, analysis and utilization, poor synergistic relationship between the Ministry of Education, UBE Board and the agencies responsible for the generation of demographic data and poor funding. The study therefore recommended among other things that there should be comprehensive policy provision to ensure effective demographic data collection, analysis and utilization, more robust administrative strategies should be adopted for data collection, analysis and utilization and a more comprehensive synergy between the Ministry of Education, UBE Board and demographic data agencies.

PPK-Means: Achieving Privacy-Preserving Clustering Over Encrypted Multi-Dimensional Cloud Data

Clustering is a fundamental and critical data mining branch that has been widely used in practical applications such as user purchase model analysis, image color segmentation, outlier detection, and so on. With the increasing popularity of cloud computing, more and more encrypted data are converging to cloud computing platforms for enjoying the revolutionary advantages of the cloud computing paradigm, as well as mitigating the deeply concerned data privacy issues. However, traditional data encryption makes existing clustering schemes no more effective, which greatly obstructs effective data utilization and frustrates the wide adoption of cloud computing. In this paper, we focus on solving the clustering problem over encrypted cloud data. In particular, we propose a privacy-preserving k-means clustering technology over encrypted multi-dimensional cloud data by leveraging the scalar-product-preserving encryption primitive, called PPK-means. The proposed technique is able to achieve efficient multi-dimensional data clustering as well to preserve the confidentiality of the outsourced cloud data. To the best of our knowledge, our work is the first to explore the privacy-preserving multi-dimensional data clustering in the cloud computing environment. Extensive experiments in simulation data-sets and real-life data-sets demonstrate that our proposed PPK-means is secure, efficient, and practical.

I181] Patient radiation exposure monitoring in medical imaging

Purpose The IAEA GSR Part 3, Radiation Protection and Safety of Radiation Sources: International Basic Safety Standards has placed requirements on managing patient exposure from medical uses of ionizing radiation. These pertain to performing and documenting dosimetry of patients following internationally accepted or nationally accepted protocols, determining typical doses to patients for common diagnostic radiological procedures and for image guided interventional procedures, and performing regular local assessments, based on the measurements, for those radiological procedures for which Diagnostic Reference Levels (DRLs) have been established. Methods The IAEA Technical meetings held in April 2015 and June 2016 requested from the IAEA to prepare a publication that provides consolidated information and guidance on patient radiation exposure in medical imaging for improved patient protection. The publication is in preparation by a group of experts, involving also representatives of international organizations and WG 28 (Physics) of DICOM. Results The document will offer a comprehensive delineation of metrics used for patient exposure data across multiple imaging modalities, including relevant acquisition parameters, exposure and dose indicators, organ doses, and the derived risk related quantities. Strategies for recording and collecting of these metrics using coding systems with a view towards ensuring data accuracy and integrity will be explicitly addressed. Considering the significant need for meaningful systematic analyses of exposure data, the document will delineate strategies on how the exposure data should be evaluated, analyzed, and understood for use towards optimization of radiation protection of patients in medical imaging. The analysis includes the methods for establishing DRLs and typical doses and trends towards improved operational consistency. It further addresses ascertaining exposure information for individual patients for error investigation and tracking exposure history for individualized justification and optimization. In addition to metrology and analysis, the document will offer description of electronic and manual strategies for data management and workflow, as well as the needed features, access, and interface for effective utilization of patient exposure data monitoring systems, considering the needs and constraints of less-resourced countries. Conclusions It is expected that the publication will support the implementation of the BSS requirements.

Identity-Based Private Matching over Outsourced Encrypted Datasets

With wide use of cloud computing and storage services, sensitive information is increasingly centralized into the cloud to reduce the management costs, which raises concerns about data privacy. Encryption is a promising way to maintain the confidentiality of outsourced sensitive data, but it makes effective data utilization to be a very challenging task. In this paper, we focus on the problem of private matching over outsourced encrypted datasets in identity-based cryptosystem that can simplify the certificate management. To solve this problem, we propose an Identity-Based Private Matching scheme ( $\mathsf{IBPM}$ ), which realizes fine-grained authorization that enables the privileged cloud server to perform private matching operations without leaking any private data. We present the rigorous security proof under the Decisional Linear Assumption and Decisional Bilinear Diffie-Hellman Assumption. Furthermore, through the analysis of the asymptotic complexity and the experimental evaluation, we verify that the cost of our $\mathsf{IBPM}$ scheme is linear to the size of the dataset and it is more efficient than the existing work of Zheng and Xu [30] . Finally, we apply our $\mathsf{IBPM}$ scheme to build two efficient schemes, including identity-based fuzzy private matching as well as identity-based multi-keyword fuzzy search.

Fuzzy keyword search over encrypted data in cloud computing

As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. This significant drawback makes existing techniques unsuitable in cloud computing as it is greatly affect system usability, rendering user searching experiences very frustrating and system efficiency very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud while maintaining keyword privacy. In our solution, we exploit edit distance to quantify keyword similarity and develop new advanced technique on constructing fuzzy keyword sets which greatly reduces the storage and representation overheads. In this way, we show that our proposed solution is secure and privacy preserving, while realizing the goal of fuzzy keyword search.

RETRACTED ARTICLE: An automated and intelligent Parkinson disease monitoring system using wearable computing and cloud technology

This paper exhibits the outline and advancement of a pervasive remote monitoring system for the Parkinson’s disease (PD) patients. The proposed system gathers various PD related information such as voice samples, gait information etc. and would empower in-home monitoring of early PD symptoms. We accomplished this objective by utilizing various wearable sensors technology, mobile computing system, Internet, cloud computing technologies. Such an incorporated framework guarantees the compelling and effective utilization of data gathered for evaluating early PD symptom’s as well as identifies critical PD severity levels. In particular, the proposed system can evaluate PD patients’ voice disorders or Dysphonia and thus enables doctors to detect patient’s PD symptoms or severity levels. Trial comes about demonstrate that our proposed system achieves very high accuracy for detecting PD symptoms as compared to existing approaches.

Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification.

Effective utilization of multi-center data for autism spectrum disorder (ASD) diagnosis recently has attracted increasing attention, since a large number of subjects from multiple centers are beneficial for investigating the pathological changes of ASD. To better utilize the multi-center data, various machine learning methods have been proposed. However, most previous studies do not consider the problem of data heterogeneity (e.g., caused by different scanning parameters and subject populations) among multi-center datasets, which may degrade the diagnosis performance based on multi-center data. To address this issue, we propose a multi-center low-rank representation learning (MCLRR) method for ASD diagnosis, to seek a good representation of subjects from different centers. Specifically, we first choose one center as the target domain and the remaining centers as source domains. We then learn a domain-specific projection for each source domain to transform them into an intermediate representation space. To further suppress the heterogeneity among multiple centers, we disassemble the learned projection matrices into a shared part and a sparse unique part. With the shared matrix, we can project target domain to the common latent space, and linearly represent the source domain datasets using data in the transformed target domain. Based on the learned low-rank representation, we employ the k-nearest neighbor (KNN) algorithm to perform disease classification. Our method has been evaluated on the ABIDE database, and the superior classification results demonstrate the effectiveness of our proposed method as compared to other methods.

30th Annual Drug Information Association (DIA) Europe 2018 (April 17-19, 2018 - Basel, Switzerland).

The Drug Information Association (DIA) Europe held its annual meeting from April 17-19, 2018, in Basel, Switzerland. The key topics discussed in the 3-day meeting were related to pharmacovigilance, clinical development, patient engagement, data and data standards, preclinical development and early-phase clinical research, regulatory science, translational medicine and science, and value and access. The program was principally focused on the current opportunities and future landscape of the healthcare system as a result of the increasingly innovative technologies and effective utilization of big data. In addition, the critical need for collaboration and partnership between all the stakeholders of the healthcare system was highlighted. This report covers some of the regulatory sessions presented at the meeting in which regulators, payers, industry and patients presented their perspectives for discussion.

Efficient and Provable Security Searchable Asymmetric Encryption in the Cloud

Cloud and remote storage, which has become the preferred method for reducing the security risk and safeguarding our data, is widely used for backups or outsourcing. Thus, for effective utilization of data, it is desirable to perform keyword queries directly over the encrypted data. In the single-data owner architecture, it is common to use symmetric encryption cryptography and allows only the holder who has the secret key to create searchable ciphertexts and trapdoors. In the multi-data owner (MDO) architecture, the scheme uses asymmetric encryption cryptography and allows anyone to create searchable ciphertexts under the public key, while the corresponding private key creates the trapdoors. In the real world, the data come from different providers. Thus, the MDO architecture may meet more applications. However, the existing solutions, in the MDO architecture, do not guarantee security for the trapdoors and provide only linear time search. In this paper, we present a comprehensive study of searching over encrypted data in the MDO architecture, and we propose a novel scheme that supports the inverted index structure, complex search, and dynamic update. After extensive analysis and several experiments, the results indicate that our proposed method is both efficient and secure. We believe that this is the first work that achieves trapdoor security and logarithmic time pairing free search over encrypted data in the MDO architecture.

Fuzzy Based Implementation of Multi - Keyword Ranked Search over Encrypted Cloud Data in Secure Cloud Environment

Cloud computing enables the paradigm of data outsourcing. However, to protect data privacy, sensitive cloud data have to be encrypted before outsourced to the commercial public cloud, which makes effective data utilization service is a challenging task. Even though searchable encryption techniques allow users to securely search over encrypted data through keywords, they support only Boolean search and are not yet sufficient to meet the effective data utilization need that is inherently demanded by large number of users and huge amount of data files in cloud. Hence it is necessary to allow multiple keywords in the search request and return documents in the order of their relevance to the keywords. The works on searchable encryption focused on single keyword search or Boolean keyword search and the result produced by them are rarely sorted. An effective method proposed for this challenging problem is privacy-preserving search over encrypted cloud data. This method establishes a set of strict privacy requirements for such a secure cloud data utilization system through MRSE. Among various multi-keyword semantics, this method chooses the efficient similarity measure of “coordinate matching”. Then according to Top K Query method the sorted results are produced. The privacy is preserved by the chunk of data stored in a various server’s. Then further improvisation is taken to introduce low overhead on computation and communication in future.

Time-Constrained Big Data Transfer for SDN-Enabled Smart City

With advanced ICT, the ever-rapid development of informatization has become an integral part of smart city services in healthcare, transportation, energy, education, business, community life, and so on. A huge amount of data, called big data, is generated from various sources, and effective analysis and utilization of big data has become a key factor in the success of smart city services. However, in order to achieve precise big data analytics and make real-time decisions, one of the challenging issues is how to efficiently deliver the huge amounts of collected data to the processing servers. In this article, we first propose a novel architecture to support smart city services based on SDN technology. Then we study the time-constrained big data transfer scheduling (TBTS) problem under the proposed architecture, and present an intelligent strategy to address the TBTS issue by utilizing the SDN controller to conduct dynamic flow control and multi-path transfer scheduling. Simulation results demonstrate that the proposed strategy can efficiently support big data transfer in terms of low transfer delay and high bandwidth utilization.

A Robust Automatic Ionospheric O/X Mode Separation Technique for Vertical Incidence Sounders

AbstractThe sounding of the ionosphere by a vertical incidence sounder (VIS) is the oldest and most common technique for determining the state of the ionosphere. The automatic extraction of relevant ionospheric parameters from the ionogram image, referred to as scaling, is important for the effective utilization of data from large ionospheric sounder networks. Due to the Earth's magnetic field, the ionosphere is birefringent at radio frequencies, so a VIS will typically see two distinct returns for each frequency. For the automatic scaling of ionograms, it is highly desirable to be able to separate the two modes. Defence Science and Technology Group has developed a new VIS solution which is based on direct digital receiver technology and includes an algorithm to separate the O and X modes. This algorithm can provide high‐quality separation even in difficult ionospheric conditions. In this paper we describe the algorithm and demonstrate its consistency and reliability in successfully separating 99.4% of the ionograms during a 27 day experimental campaign under sometimes demanding ionospheric conditions.