Set Of Data Items Research Articles

Dataset for the reporting of carcinoma of the exocrine pancreas: recommendations from the International Collaboration on Cancer Reporting (ICCR).

Current guidelines for pathology reporting on pancreatic cancer differ in certain aspects, resulting in divergent reporting practices and a lack of comparability of data. Here, we report on a new international dataset for pathology reporting on resection specimens with cancer of the exocrine pancreas (ductal adenocarcinoma and acinar cell carcinoma). The dataset was produced under the auspices of the International Collaboration on Cancer Reporting (ICCR), which is a global alliance of major (inter)national pathology and cancer organisations. According to the ICCR's rigorous process for dataset development, an international expert panel consisting of pancreatic pathologists, a pancreatic surgeon and an oncologist produced a set of core and non-core data items based on a critical review and discussion of current evidence. Commentary was provided for each data item to explain the rationale for selecting it as a core or non-core element and its clinical relevance, and to highlight potential areas of disagreement or lack of evidence, in which case a consensus position was formulated. Following international public consultation, the document was finalised and ratified, and the dataset, which includes a synoptic reporting guide, was published on the ICCR website. This first international dataset for cancer of the exocrine pancreas is intended to promote high-quality, standardised pathology reporting. Its widespread adoption will improve the consistency of reporting, facilitate multidisciplinary communication, and enhance the comparability of data, all of which will help to improve the management of pancreatic cancer patients.

Ayurveda dataset items in clinical trial registry of India

Ayurveda dataset items in clinical trial registry of India

HEAD (AND NECK) IN THE CLOUDS: RE-IMAGINING ROYAL COLLEGE CANCER DATA SETS IN A “WORDCLOUD” FORMAT

<h3>Background</h3> Royal College cancer data sets ideally should present the pathologist not only with lists of core data items for histopathologic reporting of cancers but also communicate the strengths and limitations of the evidence behind those core items. Within the confines of a text- or proforma-based representation, there is little scope for interaction with or encouragement of scrutiny of the evidence base by the pathologist. <h3>Objective</h3> Wordclouds can be used to represent textual data in a visual format, highlighting frequency and defined categories of items. We wanted to explore whether the core data items in cancer data sets could be recreated in a more visual and interactive format using a "wordcloud" approach. <h3>Methods</h3> Using the 2013 data set for histopathology reporting of mucosal malignancies of the pharynx as a test case, we extracted the evidence base cited within the document for a subset of the core data items and updated the evidence by systematic review of the literature from year of publication to present. <h3>Results</h3> We created an HTML-based wordcloud "within" a head and neck sagittal section shape outline that represents (1) each core data item, (2) the strength of evidence (text size), (3) whether that evidence is direct or inferred (text color), and (4) hyperlinks from each core data item to text-based information for that item and relevant publications from the evidence base. <h3>Conclusions</h3> We believe the dataset wordcloud might enable easier navigation and clarity of the core data items and better understanding and scrutiny of the evidence base behind them.

Calling for improved quality in the registration of traditional Chinese medicine during the public health emergency: a survey of trial registries for COVID-19, H1N1, and SARS

ObjectiveTo assess the registration quality of traditional Chinese medicine (TCM) clinical trials for COVID-19, H1N1, and SARS.MethodWe searched for clinical trial registrations of TCM in the WHO International Clinical Trials Registry Platform (ICTRP) and Chinese Clinical Trial Registry (ChiCTR) on April 30, 2020. The registration quality assessment is based on the WHO Trial Registration Data Set (Version 1.3.1) and extra items for TCM information, including TCM background, theoretical origin, specific diagnosis criteria, description of intervention, and outcomes.ResultsA total of 136 records were examined, including 129 severe acute respiratory syndrome coronavirus 2 (COVID-19) and 7 H1N1 influenza (H1N1) patients. The deficiencies in the registration of TCM clinical trials (CTs) mainly focus on a low percentage reporting detailed information about interventions (46.6%), primary outcome(s) (37.7%), and key secondary outcome(s) (18.4%) and a lack of summary result (0%). For the TCM items, none of the clinical trial registrations reported the TCM background and rationale; only 6.6% provided the TCM diagnosis criteria or a description of the TCM intervention; and 27.9% provided TCM outcome(s).ConclusionOverall, although the number of registrations of TCM CTs increased, the registration quality was low. The registration quality of TCM CTs should be improved by more detailed reporting of interventions and outcomes, TCM-specific information, and sharing of the result data.

A Framework of Hypergraph-Based Data Placement Among Geo-Distributed Datacenters

Data-intensive applications need to address the problem of properly placing the set of data items in geo-distributed storage nodes. Traditional techniques use the hashing method to achieve the load balance among nodes such as those used in Hadoop and Cassandra, but are not efficient for the requests reading multiple data items in one transaction, especially when the source locations of requests are also distributed. Some recent papers proposed the managed data placement schemes for online social networks, but have a limited scope of applications due to their focuses. We propose a general hypergraph-based data placement framework, which considers both the performance metrics related to the co-location of associated data and those related to the exact location of fulfilling each requested data item. In the framework, we present the methods to convert the optimization objectives into hypergraph models and employ a hypergraph partitioning to efficiently partition the set of data items and place them in distributed nodes. Further, we extend the scheme into replica placement where we need to find multiple locations to place the replicas of the same data item. Through extensive experiments based on trace-based datasets, we evaluate the performance of the proposed framework and demonstrate its effectiveness.

Data-driven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs

As increasingly more semantic real-world data is stored in knowledge graphs, providing intuitive and effective query methods for end-users is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF question/answering (Q/A) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. In this paper, we propose a novel data-driven graph similarity framework for RDF Q/A to extract the query graph patterns directly from the knowledge graph instead of constructing them with semantically mapped items. An uncertain question graph is presented to model the interpretations of an NLQ, based on which our problem is reduced to a graph alignment problem. In formulating the alignment, both the lexical and structural similarity of graphs are considered, hence, the target RDF subgraph is used as a query graph pattern to construct the final query. We create a pruned entity graph dynamically based on the complexity of an input question to reduce the search space on the knowledge graph. Moreover, to reduce the calculating cost of the graph similarity, we compute the similarity scores only for same-distance graph elements and equip the process with an edge association-aware surface form extraction method. Empirical studies over real datasets indicate that our proposed approach is flexible and effective as it outperforms state-of-the-art methods significantly.

An Attribution of Cyberattack using Association Rule Mining (ARM)

With the rapid development of computer networks and information technology, an attacker has taken advantage to manipulate the situation to launch a complicated cyberattack. This complicated cyberattack causes a lot of problems among the organization because it requires an effective cyberattack attribution to mitigate and reduce the infection rate. Cyber Threat Intelligence (CTI) has gain wide coverage from the media due to its capability to provide CTI feeds from various data sources that can be used for cyberattack attribution. In this paper, we study the relationship of basic Indicator of Compromise (IOC) based on a network traffic dataset from a data mining approach. This dataset is obtained using a crawler that is deployed to pull security feed from Shadowserver. Then an association analysis method using Apriori Algorithm is implemented to extract rules that can discover interesting relationship between large sets of data items. Finally, the extracted rules are evaluated over the factor of interestingness measure of support, confidence and lift to quantify the value of association rules generated with Apriori Algorithm. By implementing the Apriori Algorithm in Shadowserver dataset, we discover some association rules among several IOC which can help attribute the cyberattack.

UnifyDR: A Generic Framework for Unifying Data and Replica Placement

The advent of (big) data management applications operating at Cloud scale has led to extensive research on the data placement problem. The key objective of data placement is to obtain a partitioning (possibly allowing for replicas) of a set of data-items into distributed nodes that minimizes the overall network communication cost. Although replication is intrinsic to data placement, it has seldom been studied in combination with the latter. On the contrary, most of the existing solutions treat them as two independent problems, and employ a two-phase approach: (1) data placement, followed by (2) replica placement. We address this by proposing a new paradigm, CDR , with the objective of c ombining d ata and r eplica placement as a single joint optimization problem. Specifically, we study two variants of the CDR problem: (1) CDR-Single , where the objective is to minimize the communication cost alone, and (2) CDR-Multi , which performs a multi-objective optimization to also minimize traffic and storage costs. To unify data and replica placement, we propose a generic framework called UnifyDR , which leverages overlapping correlation clustering to assign a data-item to multiple nodes, thereby facilitating data and replica placement to be performed jointly. We establish the generic nature of UnifyDR by portraying its ability to address the CDR problem in two real-world use-cases, that of join-intensive online analytical processing (OLAP) queries and a location-based online social network (OSN) service. The effectiveness and scalability of UnifyDR are showcased by experiments performed on data generated using the TPC-DS benchmark and a trace of the Gowalla OSN for the OLAP queries and OSN service use-case, respectively. Empirically, the presented approach obtains an improvement of approximately 35% in terms of the evaluated metrics and a speed-up of 8 times in comparison to state-of-the-art techniques.

Improved Tweets Polarity Detection using Lexicon-based Features and Caching

Tweet Polarity detection the process of observing and identifying the sentiment inclination of text, whether it is positive or negative. In this paper, improved polarity detection on tweets using supervised learning is proposed. This method is using data sets available in public. The pre-processing is improved using proper caching of data items to save the time for processing of duplicate items in data sets. The feature selection strategy ensures reduced dimensionality. The low dimension data improves the classification efficiency. The experiment shows that the method is improving the overall performance in training and testing of polarity detection.

Prediction of Seismic zone in India using Neural Network Algorithms

In this world, earthquake is a major catastrophe which creates huge amount of loss in living non living things. The prediction of an earthquake is an important task in seismology. Neural network performs a key task in the prediction of earthquake. The neural network architecture are created with different input layer and hidden layers with deep learning optimization algorithms. The input layer was developed with the parameters of historical earthquake data of India taken from India Meteorological Department (IMD). The earthquake event such as date, latitude, longitude, depth, magnitude are mathematically converted into seismic indicators depend on Gutenberg-Richter’s inverse law, are the input layers of this neural network model. The developed network model was trained with set of data items using neural network algorithms such as Backpropagation and sequential learning. The Backpropagation is used to find the magnitude prediction and sequential learning is used to find the prediction model for the cartographic risky areas. The loss and accuracy of the model are analyzed with the help of software tool, Disaster Management System which is developed for this work using Python. The deep neural network optimizers such as Stochastic Gradient Descent (SGD), Adaptive Gradient algorithm (AdaGrad) and Root Mean Square propagation (RMSprop) are used to optimize the prediction model. The optimizer produced earthquake prediction model with high ability and more accuracy. Also give the cartography which shows the seismic zone in India face earthquake in future.

Unbalanced private set intersection cardinality protocol with low communication cost

Private set intersection cardinality (PSI-CA) allows two parties, the sender and receiver, to compute the cardinality of the intersection, without revealing anything more to the other party. This paper focuses on the unbalanced private data sets case, where two parties hold sets of private data items, such as the users’ identifiers; and where the size of the receiver’s private data set is significantly smaller than the size of the sender’s private data set. Two parties want to learn the cardinality of the intersection, but nothing else. The commutative encryption inspires authors to develop a novel protocol to solve the problem. Furthermore, by the application of the Bloom filter, the receiver can compute the output more easily than by the method that the encryption is carried out on the sender’s private data set when low-power mobile IoT devices are used. In the semi-honest model, we can prove the security of our protocol when the sender’s data set is big enough. The experiment shows the deviation of our protocol is negligible and the computation costs of our protocol.

Poisson-process limit laws yield Gumbel max-min and min-max.

"A chain is only as strong as its weakest link" says the proverb. But what about a collection of statistically identical chains: How long till all chains fail? The answer to this question is given by the max-min of a matrix whose (i,j) entry is the failure time of link j of chain i: take the minimum of each row, and then the maximum of the rows' minima. The corresponding min-max is obtained by taking the maximum of each column, and then the minimum of the columns' maxima. The min-max applies to the storage of critical data. Indeed, consider multiple backup copies of a set of critical data items, and consider the (i,j) matrix entry to be the time at which item j on copy i is lost; then, the min-max is the time at which the first critical data item is lost. In this paper we address random matrices whose entries are independent and identically distributed random variables. We establish Poisson-process limit laws for the row's minima and for the columns' maxima. Then, we further establish Gumbel limit laws for the max-min and for the min-max. The limit laws hold whenever the entries' distribution has a density, and yield highly applicable approximation tools and design tools for the max-min and min-max of large random matrices. A brief of the results presented herein is given in: Gumbel central limit theorem for max-min and min-max [Eliazar, Metzler, and Reuveni, Phys. Rev. E 100, 020104 (2019)10.1103/PhysRevE.100.020104].

School and Community - based Youth Mental Health Intervention Studies in India: A Scoping Review

BACKGROUND About 70% of mental disorders emerge in late childhood and young peo-ple bear the burden of these disorders throughout life. Yet, to date there has been com-paratively little research on mental health interventions for young people in India and not many attempts have been made to collate the existing literature. This systematic review aims to synthesize the available evidence on school- and community-based mental health interventions for young people in India. METHODS A range of major electronic databases were searched systematically, and the abstracts of relevant papers were independently examined for possible inclusion. Selected papers were read in full text and a standardized set of data items were extracted. RESULTS Four papers met inclusion criteria for the analysis; two studies of school-based interventions for adolescents and two studies evaluating out-of-school community interventions for youth were reviewed. The quality of evidence from the interventions in Indian school and community settings were poor. While two studies evidence the effectiveness of a school-based life skills programme and a community based multicomponent intervention designed to promote youth health, two other studies do not offer sufficient data. CONCLUSION The review findings indicate that the number of interventional studies conducted in India to address youth mental health issues are very limited. Hence, it is extremely difficult to ensure the feasibility and effectiveness of school and community-based interventions in India. Further research is warranted to establish whether interventions promoting youth mental health people can be implemented effectively in Indian settings with positive mental health outcomes. Given the possibility of a huge population of young people at-risk or experiencing mental disorders, evidence for the efficacy of youth mental health interventions is crucial.

School and Community - based Youth Mental Health Intervention Studies in India: A Scoping Review

BACKGROUND About 70% of mental disorders emerge in late childhood and young peo-ple bear the burden of these disorders throughout life. Yet, to date there has been com-paratively little research on mental health interventions for young people in India and not many attempts have been made to collate the existing literature. This systematic review aims to synthesize the available evidence on school- and community-based mental health interventions for young people in India. METHODS A range of major electronic databases were searched systematically, and the abstracts of relevant papers were independently examined for possible inclusion. Selected papers were read in full text and a standardized set of data items were extracted. RESULTS Four papers met inclusion criteria for the analysis; two studies of school-based interventions for adolescents and two studies evaluating out-of-school community interventions for youth were reviewed. The quality of evidence from the interventions in Indian school and community settings were poor. While two studies evidence the effectiveness of a school-based life skills programme and a community based multicomponent intervention designed to promote youth health, two other studies do not offer sufficient data. CONCLUSION The review findings indicate that the number of interventional studies conducted in India to address youth mental health issues are very limited. Hence, it is extremely difficult to ensure the feasibility and effectiveness of school and community-based interventions in India. Further research is warranted to establish whether interventions promoting youth mental health people can be implemented effectively in Indian settings with positive mental health outcomes. Given the possibility of a huge population of young people at-risk or experiencing mental disorders, evidence for the efficacy of youth mental health interventions is crucial.

SpeCH: A scalable framework for data placement of data-intensive services in geo-distributed clouds

The advent of big data analytics and cloud computing technologies has resulted in wide-spread research on the data placement problem. Since data-intensive services require access to multiple datasets within each transaction, traditional schemes of uniformly partitioning the data into distributed nodes, as employed by many popular data stores like HDFS or Cassandra, may cause network congestion thereby affecting system throughput. In this article, we propose a scalable and unified framework for data-intensive service data placement into geographically distributed clouds. The proposed framework introduces a new paradigm for partitioning a set of data-items into geo-distributed clouds using Spectral Clustering on Hypergraphs, and is therefore called SpeCH. Scaling spectral methods to large workloads is challenging, since computing the spectra of the hypergraph laplacian is a computationally intensive task. SpeCH provides two solutions to tackle this problem: (1) an algorithm, called SpectralApprox, that leverages randomized techniques for obtaining low-rank approximations of the hypergraph matrix with bounded guarantees, thereby significantly improving the efficiency of spectral clustering while also providing high quality solutions in practice; (2) an algorithm, called SpectralDist, that exploits the highly parallel nature of the spectral clustering algorithm and uses Apache Spark to speed-up the process while retaining the same quality guarantees as the exact algorithm. Additionally, being distributed in nature, SpectralDist enables SpeCH to perform data placement on workloads that require resources beyond the capacity of a single machine. Experiments on a real-world trace-based online social network dataset show that the SpeCH is effective, efficient, and scalable. Empirically, SpectralApprox is comparable in efficacy on the evaluated metrics, while being up to 10 times faster in execution time when compared to state-of-the-art techniques. On the other hand, though SpectralApprox is 7–8 times faster when compared to SpectralDist, in terms of efficacy on the evaluated metrics the latter is up to 50% better.

MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support

In practice, single item support cannot comprehensively address the complexity of items in large datasets. In this study, we propose a big data analytics framework (named Multiple Item Support Frequent Patterns, MISFP-growth algorithm) that uses Hadoop-based parallel computing to achieve high-efficiency mining of itemsets with multiple item supports (MIS). The proposed architecture consists of two phases. First, in the counting support phase, a Hadoop MapReduce architecture is employed to determine the support for each item. Next, in the analytics phase, sub-transaction blocks are generated according to MIS and the MISFP-growth algorithm identifies the frequency of patterns. To facilitate decision makers in setting MIS, we also propose the concept of classification of item (COI), which classifies items of higher homogeneity into the same class, by which the items inherit class support as their item support. Three experiments were implemented to validate the proposed Hadoop-based MISFP-growth algorithm. The experimental results show approximately 38% reduction in the execution time on parallel architectures. The proposed MISFP-growth algorithm can be implemented on the distributed computing framework. Furthermore, according to the experimental results, the enhanced performance of the proposed algorithm indicates that it could have big data analytics applications.

On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identification

AbstractSequential pattern mining (SPM) is a well‐studied theme in data mining, in which one aims to discover common sequences of item sets in a large corpus of temporal itemset data. Due to the sequential nature of data streams, supporting SPM in streaming environments is commonly studied in the area of data stream mining as well. On the other hand, stream‐based process discovery (PD), originating from the field of process mining, focusses on learning process models on the basis of online event data. In particular, the main goal of the models discovered is to describe the underlying generating process in an end‐to‐end fashion. As both SPM and PD use data that are comparable in nature, that is, both involve time‐stamped instances, one expects that techniques from the SPM domain are (partly) transferable to the PD domain. However, thus far, little work has been done in the intersection of the two fields. In this focus article, we therefore study the possible application of SPM techniques in the context of PD. We provide an overview of the two fields, covering their commonalities and differences, highlight the challenges of applying them, and, present an outlook and several avenues for future work.This article is categorized under: Algorithmic Development &gt; Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge &gt; Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge &gt; Big Data Mining

Recommended configuration for personal health records by standardized data item sets for diabetes mellitus and associated chronic diseases: A report from Collaborative Initiative by six Japanese Associations.

It is expected that a large amount of data related to diabetes and other chronic diseases will be generated. However, databases constructed without standardized data item sets can be limited in their usefulness. To address this, the Collaborative Committee of Clinical Informatization in Diabetes Mellitus was established in 2011 by the Japan Diabetes Society and Japan Association for Medical Informatics. The committee has developed core item sets and self‐management item sets for diabetes mellitus, hypertension, dyslipidemia, and chronic kidney disease in collaboration with the Japanese Society of Hypertension, Japan Atherosclerosis Society, Japanese Society of Nephrology, and Japanese Society of Laboratory Medicine, as well as a mapping table that aligns the self‐management item sets with the Japanese standardized codes for laboratory testing. The committee also determined detailed specifications for implementing the four self‐management item sets in personal health record (PHR) applications to facilitate risk stratification, the generation of alerts using information and communications technology systems, the avoidance of data input errors, and the generation of reminders to input the self‐management item set data. The approach developed by the committee may be useful for combining databases for various purposes (such as for clinical studies, patient education, and electronic medical record systems) and for facilitating collaboration between PHR administrators.

Bulk-loading and bulk-insertion algorithms for $$\hbox {xBR}^{+}\hbox {-trees}$$ xBR + -trees in Solid State Drives

Spatial indexes are important in spatial databases for efficient execution of queries involving spatial constraints. The $$\hbox {xBR}^{+}\hbox {-tree}$$ is a balanced, disk-resident, Quadtree-based index for point data, which is very efficient for processing spatial queries. Bulk-loading refers to the process of creating an index from scratch as a whole, when the dataset to be indexed is available beforehand, instead of creating (loading) the index gradually, when the dataset items are available one-by-one. Bulk insertion refers to the process of updating an existing index by inserting a large batch of new data, treating the items of this batch as a whole and not by inserting these items one-by-one. In this paper, we modify previous bulk-loading and bulk-insertion algorithms for $$\hbox {xBR}^{+}\hbox {-trees}$$ to achieve higher performance by taking advantage of the special features of Solid State Drives (SSDs). SSDs have attracted database developers, mainly due to their higher read performance (thanks to their internal parallelism) than Hard Disk Drives. Using real and artificial datasets of various cardinalities, we experimentally compare the modified algorithms against their predecessors and show that the modified algorithms are clear winners regarding performance.

Query expansion based on clustering and personalized information retrieval

Information retrieval systems are used to describe a variety of processes involving the delivery of information to people who need it. Although several mathematical approaches have been studied in order to formalize the main components of an information retrieval system: queries representation, information items representations and the retrieval process, such systems still face many difficulties to extract relevant information for users especially when the processed data are texts. This is due to the complex nature of text databases. Generally, an information retrieval system reformulates queries according to associations among information items before matching them to dataset items. In this sense, semantic relationships or machine learning techniques can be applied to refine the returned results. This paper presents a formal model to organize data, and a new search algorithm to browse it. It incorporates a natural language preprocessing stage, a statistical representation of short documents and queries and a machine learning model to select relevant results. We propose later in this paper two further optimizations that proved quite interesting and returned significantly satisfying results on two datasets in a reasonable computation time. The first optimization concerns queries expansions, while the second one concerns dataset restructuration. Thus, we formally evaluate the impact of each optimization by computing the performance of the information retrieval system with and without it; the highest reached recall and precision were 96.2% and 99.2%, respectively.