Distributed Processing Research Articles

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

Competition Law and Policy in the Global South: Power, Coercion and Distribution

Abstract Countries in the Global South have adopted competition laws and pursued competition policies very similar to countries in the North. This arrangement can be traced to various coercive powers at play—from trading partners in the North, international organizations to development banks, among others. As a result, the adopted laws are often unsuitable to the local needs of the countries in the South and their enforcement policies are often shaped by global pressure. This has alienated countries in the Global South from pursuing competition enforcement policies that could be empowering to their firms, consumers and communities at large. One way to resist and challenge these coercive powers is to pursue alternative competition policies, not alien to the Western nations themselves. In these alternative configurations, competition laws are squared with goals of industrialization and distributive equality. Pursuing these alternative competition goals challenges the dominance of the static model of competition policy aiming to achieve allocative efficiency. Examples from many places around the world are illustrated to show how competition policy, at crucial times of their development, were broadened to encompass an industrial agenda. The latter, more suitable for countries in the South, is discussed as a means of counter-coercion. It is discussed alongside an elaborate program for distribution to assure that the benefits of industrialization do not befall upon only a few. The aim of such a distributive program, built into competition enforcement, is to bring social justice concerns within the purview of competition policy.

Eight Years of Canadian Real-World Assessment of Tolvaptan in ADPKD: C-MAJOR Study and Safety Monitoring and Distribution Program

Eight Years of Canadian Real-World Assessment of Tolvaptan in ADPKD: C-MAJOR Study and Safety Monitoring and Distribution Program

RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing

In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, a Region-Aware framework for geo-distributed graph processing. At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Finally, the experiments show that RAGraph can achieve 1.69X - 40.53X speedup and 20.9% - 97% WAN cost reduction compared with state-of-the-art systems.

Performance Measurement of Zakat Utilization: The Effectiveness of Zakat Distribution

In this study, we will examine the performance of zakat. The background of the research is the zakat utilization program as one of the programs in the zakat intermediation role, performance must be assessed to see the sustainability of the program. One of them is the effectiveness of the zakat distribution program. The research method used is a case study of zakat programs (BAZNAS) in West Java area, with a descriptive analysis approach. Data collection techniques used were observation, in-depth interviews and documentation. The results of this study are expected to be an innovation that becomes a reference for zakat institution managers to assess the performance of zakat utilization by looking at the effectiveness of the zakat distribution program. Zakat distribution program at BAZNAS of West Java focuses on the economy, health, and education. BAZNAS of West Java is distributed for da’wah and the victims of natural disasters. Zakat utilization at BAZNAS of West Java covers the fields of economy, health, and education. In carrying out the role as a zakat distributor, BAZNAS of West Java still encounters obstacles that hinder the effectiveness of the zakat distribution program. Many measurements are used to assess the effectiveness of its role in zakat distribution, including the implementation of standard operating procedures (SOP).
 Keywords: effectiveness, zakat utilization, zakat

Efficient continuous subgraph matching scheme considering data reuse

As the utilization of graph streams in various applications increases, a continuous subgraph matching scheme is required to search for subgraphs that change in real time. In this paper, we present a novel and effective continuous subgraph matching scheme that leverages indexing and employs distributed processing in graph stream environments. To achieve distributed processing, we employ a query graph decomposition policy based on the degree of nodes, allowing us to manage the decomposed subqueries as an index. By reusing indexing information, we significantly reduce the indexing load, which becomes crucial in scenarios where multiple queries are issued simultaneously. To optimize query allocation in the distributed environment, we introduce a cost model that accurately calculates the indexing load for each server. This ensures a balanced distribution of queries, enhancing overall system performance. To perform distributed processing efficiently in stream environments, the proposed scheme is implemented in Storm. We conduct various performance evaluations to demonstrate the superiority of the proposed scheme.

A GIS-based land suitability model for agricultural tractors in CALABARZON Region, Philippines

Agricultural machinery distribution programs are key components of the Philippine government to strengthen its economic productivity. However, concerns were reported that some of the distributed machines were under-utilized or unutilized in their respective farms. This study is focused on the development of a GIS-based model of determining suitable lowland rice areas for two-wheel and four-wheel tractors. A numerical rating system was designed that assigns a suitability score from three criteria, namely slope, road network proximity, and flood risk, based on established scales. Analytical Hierarchy Process was used as an approach to determine the relative influence in which results show that slope has the greatest weight (65%) followed by flood risk and road network proximity with 20% and 15%, respectively. The total suitability score (TSS) is then determined by getting the weighted average of the individual suitability scores. Land suitability mapping on the study area, the CALABARZON region, show that majority of the lowland rice areas are highly suitable for two-wheel and four-wheel tractors (81.39%) while some areas fall under marginal (15.03%) and moderate suitability (3.58%). Geotagged locations of the distributed agricultural tractors from 2015 to 2020 reveal that 78% of the distributed two-wheel tractors and 80% of four-wheel tractors in the region are situated in highly suitable areas. The GIS-based suitability model generated in this study can be utilized by the government to improve its machine distribution programs of two-wheel and four-wheel tractors in lowland rice farms.

The Center for Indigenous Innovation and Health Equity: The Osage Nation's Mobile Market.

Over the last decade, the Osage Nation has actively worked to build Tribal food sovereignty within the reservation where rates of chronic disease and food insecurity are higher than the United States general population. In 2013, the Nation repurposed land toward the development of a Tribal farm with the aim of providing healthy foods to Osage citizens. Produce from the farm is distributed to elders groups, at Tribal Head Starts and schools, and to support the tribal food distribution program. These efforts have led to improved vegetable intake among Osage children, contributing to improved food security, but there is concern that tribal members who live in more remote areas of the Nation or have transportation or mobility issues are not able to access farm production. In partnership with the Center for Indigenous Health Equity (CIIHE), Osage Nation engaged in a community-based participatory research study to assess reservation areas with the greatest barriers to healthy foods and to identify community priorities for intervention. Guided by the principles of food sovereignty, which assert that intervention efforts must address the underlying structural issues of inequality, Osage has designed a mobile market initiative to expand the reach of the Harvest Land farm and deliver healthy, tribally produced meats, herbs, and fresh vegetables to areas with the highest rates of food insecurity. We describe the participatory research efforts and evaluation strategies that center Osage priorities for food security and food sovereignty.

Implementasi Zakat Produktif Dan Konsumtif Dalam Meningkatkan Kesejahteraan Ekonomi Mustahik

This paper aims to describe the implementation of productive and consumptive zakat in improving the economic welfare of mustahik in Baznas Sidrap South Sulawesi in terms of distribution patterns, program effectiveness, and supporters and obstacles. This type of research is descriptive qualitative with observation, interview, and documentation methods. Furthermore, the data obtained will be analyzed through syar'i and juridical approaches. The results of this study show that: 1) The pattern of zakat distribution in Baznas Sidrap uses two patterns, namely productive and consumptive distribution. 2) Baznas Sidrap programs, namely: first, Sidrap Cares, namely basic food assistance, house assistance worth 50 million, mass circumcision, funeral benefits, victims of natural disasters, house renovations, and distribution of aid to Muallaf. Second, Sidrap Cerdas is assistance for completing studies, mosque councils, funds to Al-Azhar Cairo, and honorary funds. Third, Sidrap Mandiri is business capital assistance and tools for business. Fourth, Sidrap Sehat is medical funding assistance and clinic creation. Fifth, Religious Sidrap is assistance for zakat and waqf counseling activities, religious competitions, santri days, and STQH activity funds. 3) Supporters of productive and consumptive zakat are the many potentials of zakat and are supported by the government. The obstacles are that public understanding of zakat is still lacking, lack of public trust in Baznas, locations that are difficult to reach, and zakat funds are still low.

Classifying Release Notes Explanations using BERT: An Initial Step to Automatic Versioning in Distributed Software Development

Distributed Software Development is the practice of developing software with a team in different locations. The process of software versioning is crucial in distributed development as it helps in keeping track of the various software versions that are being developed and maintaining projects. In transition of each new version, the development team present release notes that inform all team members and stakeholders are aware of changes and provide tracking project progresses. Release notes consist information about the features, bug fixes, and other changes included in a new software release. Generating release notes and determining the release transition timing for new software versions can be costly. Despite of there are some papers about generating release notes in the literature, there is not any study about automatic versioning. In this context, the aim of this paper is to predict the development types in release notes as the first phase of an automated versioning tool that is planned to be built in future work. We used BERT which is one of the popular transformers to classify developments of release notes and our model has 86% accuracy rate on our own public dataset. Additionally, we presented insights on the model's decision-making process in the context of explainable AI using ELI5 library.

Toward an Effective Innovation Framework for Federal Grant-Making: an Exploration into OPA’s Teen Pregnancy Prevention Program

States, local, tribal, and territorial governments received almost 20% of all federal spending in fiscal year 2022, about $1.2 trillion dollars (US Office of Management and Budget, 2023a). For a strong, well-functioning democracy, the federal government must be able to disperse grant funds efficiently and effectively. Rates of teen pregnancy in the USA, while decreasing in recent years, are still consistently higher than that of other western industrialized nations (Centers for Disease Control and Prevention, 2023). The Office of Population Affairs’ (OPA) Teen Pregnancy Prevention program combines cutting edge research with innovative grant distribution to creatively tackle this issue. In this commentary, we explore some of the strengths of OPA’s grant distribution program in the hope that others may emulate best practices from this program. Specifically, the special issue highlights how OPA’s grant program took a customer-centered focus by prioritizing work with end users and community. This evidence-based focus enabled the testing of ideas, which in turn could be iterated and scaled up. Through grantee innovation networks, they created space for external expertise, creative thinking, and diversity of thought. Other programs, policymakers, and their partners may learn from OPA’s success, which arises from three core characteristics: (1) their funding stature allows long-term and flexible allocation of funding toward innovation; (2) OPA focuses on creating and supporting local communities and networks of innovation; (3) OPA emphasizes evidence-based decision-making and rigorous evaluation throughout the grant process. With a fifth of the federal budget being dispersed through grant-making and teen pregnancy still at high rates, OPA offers an exciting avenue for innovation and success in both of these areas. This commentary concludes with some recommendations for future practice.

Construction and Application of Trajectory Data Analysis Model Based on Big Data and Stochastic Gradient Descent Algorithm

This paper studies the model construction of computing and storage resource management system framework based on Hadoop and the implementation of trajectory data analysis function under big data. Relying on the cloud platform infrastructure, in order to support the rapid data growth and massive data processing needs, it provides a mixed storage and analysis platform for structured and unstructured data, and uses big data technology to build a highly scalable and distributed data processing framework. The distributed computation, overall frame model of the memory system, and function module have been built with the aim of constructing the system in consideration. Second, by using Hadoop to preprocess the original data and concentrating on the data hierarchical design model and key technology analysis of big data systems, the design model, functional modules, technological solutions, and SGD algorithm are suggested, along with the detailed implementation procedure. Lastly, by merging the data of running vehicles, the system accomplishes the data analysis of vehicle trajectory, empty and load cars, and load and unload people.

309 The CFTR Antibody Distribution Program: Development and production of antibodies for CFTR detection

309 The CFTR Antibody Distribution Program: Development and production of antibodies for CFTR detection

A Feedforward Interactive Network to Explain Complex Information Processing Phenomena Including the Halo Effect and Its Generalization

Abstract Parallel interactive processing (PIP) is a parallel distributed processing approach in which a representation of an object involves interaction of its components in a way that influences information processing of the components. Prior research is summarized in the areas of categorization and probability judgment. It was found that an explanation of the halo effect and its generalization as well as phenomena in other information processing situations could be explained by PIP. The explanation of the halo effect is largely in terms of reduction of variance in judgments of components of an object produced by a holistic impression of the object. There was a focus on processes common to various subject areas.

Evaluasi Program Penyaluran Dana Zakat, Infaq dan Shadaqah Kepada Pelaku Usaha

The role of the Amil Zakat Institution (Laznas) Berdaya Sejahtera Mandiri (BSM) for the Ummah is to manage the ZIS funds (zakat, infaq, shadaqah) as well as possible and as safely as possible in the form of programs that are beneficial to the people and have sustainable benefit values. The aims of the research are (1) to analyze the program process for channeling ZIS funds to business actors; (2) Analyzing the performance and development of program beneficiary mustahik; and (3) Predicting the development of the ZIS fund distribution program to business actors in the future. The research location was carried out at the BSM Village Program located in three villages, namely Rejosari Village, Lampung, Kedarpan Village, Purbalingga; Jati Village, Trenggalek. The research time spans seven months, from January to July 2022. Data processing uses strengths, weaknesses, opportunities and threats (SWOT)-Balanced Scorecard (BSC). The validity test analysis of the four perspectives shows valid results. Reliability test analysis on the four BSC perspectives is reliable (reliable). The results of the analysis of financial ratios, seen from the ratio of liquidity, solvency and profitability show quite healthy results, namely the program for channeling ZIS funds to business actors is running well, can run their business, and has good performance. The results of the SWOT-BSC analysis provide alternative strategies that can be carried out in the future, namely (1) A financial perspective by strengthening literacy programs and building an integrated service system to increase public trust; (2) Perspective of members by strengthening GCG and HR capacity, as well as socializing work programs to prospective muzakki; (3) Internal business process perspective by increasing internal marketing to BSI customers and standardizing SOPs so that the program runs optimally; (4) The perspective of learning and growth by conducting intensive assistance, as well as creating programs that are in line with the BSI program. The results of the analysis using the QSP matrix show that the priority strategy that can be implemented is to provide intensive assistance so that program beneficiaries become bankable

Comprehensive Strategies for Addressing the Opioid Epidemic: A Review of Overdose Education, Naloxone Distribution, and Suboxone Interventions

The United States is currently grappling with an opioid epidemic, prompting the development of Opioid Overdose Education and Naloxone Distribution programs (OEND) and Suboxone distribution programs as a response in certain states.

Prisma : A Tierless Language for Enforcing Contract-client Protocols in Decentralized Applications

Decentralized applications (dApps) consist of smart contracts that run on blockchains and clients that model collaborating parties. dApps are used to model financial and legal business functionality. Today, contracts and clients are written as separate programs—in different programming languages—communicating via send and receive operations. This makes distributed program flow awkward to express and reason about, increasing the potential for mismatches in the client-contract interface, which can be exploited by malicious clients, potentially leading to huge financial losses. In this article, we present Prisma , a language for tierless decentralized applications, where the contract and its clients are defined in one unit and pairs of send and receive actions that “belong together” are encapsulated into a single direct-style operation, which is executed differently by sending and receiving parties. This enables expressing distributed program flow via standard control flow and renders mismatching communication impossible. We prove formally that our compiler preserves program behavior in presence of an attacker controlling the client code. We systematically compare Prisma with mainstream and advanced programming models for dApps and provide empirical evidence for its expressiveness and performance.

Virtual Interviews in Neurosurgery Resident Selection: A Follow-up Report

The 2021 U.S. neurosurgery residency match interviews were conducted virtually; we surveyed applicants and interviewers to determine satisfaction with that virtual interview process. Subsequently, we conducted a follow-up survey to determine satisfaction with the virtual interview process after the residency match for faculty interviewers and 2022 interns. A 22-question online faculty survey was sent to 116 U.S. neurosurgery training programs. A 26-question survey was sent to these programs for distribution to their intern classes. Data were analyzed quantitatively, including mean Likert score. Open-ended questionnaire responses were reviewed to identify themes. Overall, 32 interns representing 20 programs and 73 faculty representing 62 programs responded. Most respondents agreed that virtual interviews were more convenient (86% faculty, 90% interns) and cost-effective (100% interns) than in-person interviews. Faculty respondents agreed or strongly agreed that virtual interviews were effective to evaluate applicants' competence as residents (44%); fewer faculty agreed or strongly agreed that virtual interviews were an effective way to evaluate candidates' fit in the program (27%). For interns, 44% agreed or strongly agreed that virtual interviews gave them a good sense of the program faculty; 75% agreed or strongly agreed they were satisfied with the process related to where they matched. Virtual interviews offer an advantage in terms of time and cost but potentially at the expense of adequate faculty assessment of candidates' "fit" within a program's culture. Despite this, interns undergoing an all-virtual interview process report high satisfaction with the results of the residency match.

Application of Entity-BERT model based on neuroscience and brain-like cognition in electronic medical record entity recognition.

In the medical field, electronic medical records contain a large amount of textual information, and the unstructured nature of this information makes data extraction and analysis challenging. Therefore, automatic extraction of entity information from electronic medical records has become a significant issue in the healthcare domain. To address this problem, this paper proposes a deep learning-based entity information extraction model called Entity-BERT. The model aims to leverage the powerful feature extraction capabilities of deep learning and the pre-training language representation learning of BERT(Bidirectional Encoder Representations from Transformers), enabling it to automatically learn and recognize various entity types in medical electronic records, including medical terminologies, disease names, drug information, and more, providing more effective support for medical research and clinical practices. The Entity-BERT model utilizes a multi-layer neural network and cross-attention mechanism to process and fuse information at different levels and types, resembling the hierarchical and distributed processing of the human brain. Additionally, the model employs pre-trained language and sequence models to process and learn textual data, sharing similarities with the language processing and semantic understanding of the human brain. Furthermore, the Entity-BERT model can capture contextual information and long-term dependencies, combining the cross-attention mechanism to handle the complex and diverse language expressions in electronic medical records, resembling the information processing method of the human brain in many aspects. Additionally, exploring how to utilize competitive learning, adaptive regulation, and synaptic plasticity to optimize the model's prediction results, automatically adjust its parameters, and achieve adaptive learning and dynamic adjustments from the perspective of neuroscience and brain-like cognition is of interest. Experimental results demonstrate that the Entity-BERT model achieves outstanding performance in entity recognition tasks within electronic medical records, surpassing other existing entity recognition models. This research not only provides more efficient and accurate natural language processing technology for the medical and health field but also introduces new ideas and directions for the design and optimization of deep learning models.

International harm reduction indicators are still not reached: results from a repeated cross-sectional study on drug paraphernalia distribution in Germany, 2021

BackgroundTo prevent the transmission of blood-borne infections and reach the elimination of viral hepatitis by 2030, the World Health Organization (WHO) has set the goal to distribute 300 sterile needles and syringes each year per person who injects drugs (PWID). We aimed to assess drug paraphernalia distribution in Germany in 2021, including the WHO indicator, and to analyse changes to the distribution measured in 2018.MethodsWe conducted a repeated cross-sectional study of low-threshold drug services in Germany. We assessed type and quantity of distributed drug paraphernalia and the number of supplied PWID in 2021 using an online and paper-based questionnaire. We conducted a descriptive statistical analysis of data from 2021, assessed fulfillment of the WHO indicator and changes in services that participated 2021 and in the previous study 2018.ResultsFive hundred and eighty-nine of 1760 distributed questionnaires were returned in 2021. 204 drug services from 15 out of 16 federal states confirmed drug paraphernalia distribution, covering 20% of Germany’s rural and 51% of urban counties. 108 services had also participated in 2018. The most frequently distributed paraphernalia for injecting drug use in 2021 were syringes (97% of services), needles (96%) and vitamin C (90%). Pre-cut aluminium foil (79% of services) and pipes (28%) for inhaling, and sniff tubes (43%) for nasal use were distributed less frequently. We found a median reduction in distributed syringes by 18% and by 12% for needles compared to 2018. Of 15 states, two reached the 2030 WHO-target for needles and one for syringes.ConclusionsThe current national estimates and changes from 2018 to 2021 for drug paraphernalia distribution seem far from meeting the WHO target. Reasons could include a change in drug consumption behaviour towards less injecting use and more inhaling, and effects of the COVID-19 pandemic (supply difficulties, social distancing, lockdown, reduced opening hours of services). We observed pronounced regional differences in drug paraphernalia distribution. To close existing gaps, Germany should expand its drug paraphernalia distribution programmes and other harm reduction services, such as drug consumption rooms. Further investigation of determinants for adequate distribution is essential to reduce blood-borne infections in this key population.