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检索条件"任意字段=2015 International Conference on Big Data and Smart Computing, BIGCOMP 2015"

共 2627 条记录，以下是71-80 订阅

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TPC-C Benchmarking for ElasticSearch

TPC-C Benchmarking for ElasticSearch

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Lee, JaeKyu Kwon, Hyuk-Yoon Seoul Natl Univ Sci & Technol Dept Data Sci Seoul South Korea

ISBN: (纸本)9781665421973

ElasticSearch is a distributed search and analysis engine. Even though it is a NoSQL system that supports weak consistency, it has been recently applied to financial services that require strong consistency. In this study, we propose a method to support the TPC-C benchmark for ElasticSearch. To this end, we present the Force Delete Lock (FD Lock) using built-in operations in ElasticSearch to provide the distributed lock for the transaction. Then, we implement the rollback operation in a distributed environment using FD Lock. Finally, we compare the consistency failures and the TPC-C performance of ElasticSearch with FD Lock and that without FD Lock. As a result, we show that FD Lock can completely remove the consistency failures, and the performance degradation of FD Lock decreases as the number of warehouses increases because it increases the consistency failures.

关键词： ElasticSearch Distributed Lock NoSQL Benchmark TPC-C

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Design of Ontology Model for Knowledge Navigator

Design of Ontology Model for Knowledge Navigator

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Hwang, Yun-Young Son, Jiseong Shin, Sumi Korea Inst Sci & Technol Informat Dept Intelligent Data Res Dajeon South Korea

ISBN: (纸本)9781665421973

We defined and expressed graph-based relationships of pieces of knowledge to allow cross-reference navigation of the knowledge as an ontology. We present a methodology for constructing an ontology-based knowledge navigation model to allow cross-reference navigation between pieces of knowledge, related concepts and datasets. We defined the structure of the dataset as metadata, the field names of the actual values, and the actual values. We defined the relationship between datasets as 3-layer such as metadata layer, field names layer, and data value layer. The relationships in the metadata layer was defined as the correspondence between metadata values. We standardized the field names in dataset to discover the relationships between field names. We designed a method to discover the relationship between data values based on common knowledge for each domain. To confirm the validity of the presented methodology, we applied our methodology to implement an ontology-based knowledge navigation model for actual disaster-related processes in operation. We built a knowledge navigation model based on spatial common knowledge to confirm that the configuration of the knowledge navigation model was correct.

关键词： Knowledge Navigator Ontology Model dataset Discovery

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Graph Summarization for Human-Understandable Visualization towards CVE data Analysis

Graph Summarization for Human-Understandable Visualization t...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Park, Ji Sun Kang, Mingu Lee, Sungryoul Chae, Dong-Kyu Hanyang Univ Seoul South Korea Zhejiang Univ Hangzhou Peoples R China Affiliated Inst ETRI Daejeon South Korea

ISBN: (纸本)9781665421973

Graph is one of the data structures used to represent various real-world data effectively. It is easy to analyze complex relationships between objects through graphs, and the structure of data can be understood at a glance through visualization. As the size of the data increases, the scale of the graph representing the data also increases. However, for large-scale graphs, not only is it difficult to analyze patterns, but it is also expensive in terms of storage space and computational speed. In this case, by applying a graph summary method that can extract only the core information of the graph, the aforementioned problem can be solved. This paper studies various graph summarization techniques and applied them to the CVE data. For effective analysis, we first converted the CVE data into a graph, then summarized it with graph embedding and clustering methods. By visualizing the results, we confirmed that we could successfully derive a brief view of the data through this process.

关键词： Laplace equations data analysis conferences Clustering methods data visualization Clustering algorithms Inspection

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Prediction of Defect Coffee Beans Using CNN

Prediction of Defect Coffee Beans Using CNN

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Lee, Ji-Yoon Jeong, Young-Seob Soonchunhyang Univ Dept Big Data Engn 22 Soonchunhyangro Asan South Korea Chungbuk Natl Univ Dept Comp Engn Chungdae Ro 1 Cheongju South Korea

ISBN: (纸本)9781665421973

The growing demand for coffee has led to the development to the coffee industry. Since defect coffee beans affect the taste of coffee, it is essential to select them to improve the quality of coffee. This is basically a classification task that predicts appropriate types of defect in given coffee beans. When a person is working manually for this classification, it can be affected by human condition and has the disadvantage of taking a long time. There have been few studies that utilized data-driven method to predict defect coffee beans by analyzing images, and they commonly used convolutional neural network (CNN) model as it has shown its superior performance and efficient in image classification area. This paper proposes a method to predict defects of coffee beans by applying the CNN model to the images, so we basically solve a problem of binary image classification. We achieved the accuracy of 90.44%, and we believe our model can be used systematically and efficiently in the coffee industry.

关键词： Defect Coffee Beans Deep Learning Image Classification data Imbalance Convolutional Neural Network

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Reducing Hadoop 3.x energy consumption through Energy Efficient Ethernet

Reducing Hadoop 3.x energy consumption through Energy Effici...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Bolonhezi Dias, Jorgi Luiz De Almeida, Leandro Batista Pessoa Albini, Luiz Carlos Univ Fed Parana Dept Informat Curitiba Parana Brazil Technol Fed Univ Parana Acad Dept Informat Curitiba Parana Brazil

ISBN: (纸本)9781665421973

Energy consumption is one of the major challenges on the big data processing infrastructure. The energy expenses are even higher than hardware, accounting for 75% of the total cost of nowadays data centers. Narrowing, approximately 30% of all data center energy is consumed by the network switches. Energy Efficient Ethernet is a recent standard aiming at reduce network power consumption, notwithstanding the current practice in industry is to disable it in production use, since it can cause network overloads and performance loss. This paper provides an overview on how Apache Hadoop 3.x, the current version, behaves with Energy Efficient Ethernet enabled for links from 1GbE up to 400GbE links. Presented results show that there is significant energy savings with little or no performance loss for connections up to 40GbE. Nevertheless, connections of 100GbE and 400GbE present significant performance losses due to link wake up to single transmissions. These results demonstrate the effectiveness of EEE and indicate a possible research field on the ultra high speed networks energy efficiency.

关键词： Green Networking big data Energy Efficient Ethernet MapReduce Hadoop

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Comparative Methods for Personalized Customer Churn Prediction with Sequential data

Comparative Methods for Personalized Customer Churn Predicti...

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IEEE international conference on big data and smart computing (bigcomp)

作者： Bayrak, Ahmet Tugrul Yuceturk, Guven Bahadir, Musa Berat Yalcinkaya, Sare Melek Demirdag, Melike Sayan, Ismail Utku Ata Technol Platforms Data Sci & Innovat Istanbul Turkey

ISBN: (纸本)9781665421973

Competition in the business environment has been increasing with the developed technology. There are various alternative service providers for customers. Those providers would like to retain their current customers. To reach that goal, churn prediction is a convenient method. However, predicting churning customers is not a trivial task. It is more demanding for some sectors like fast-food, since there can be numerous reasons when a customer stops having services. In this study, the challenging situation of customer churn in the fast-food industry is analyzed, and a fast-food chain's data is used. The data is formed sequentially accordingly with customers' personal churn periods. Several recurrent neural network models such as gated recurrent units and long short-term memory are built using the sequential data to predict the churn stages of customers, and they are compared with the other standard classification methods. Apart from recurrent neural network models, a hybrid model consisting of a convolutional network and long short-term memory is applied.

关键词： churn analysis sequential data deep learning recurrent neural network convolutional neural network

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Medication Monitoring from Accelerometer data through a Series of Medication Actions Using Neural Network for Medication Adherence Evaluation

Medication Monitoring from Accelerometer Data through a Seri...

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IEEE international conference on big data and smart computing (bigcomp)

作者： Ikeda, Chihiro Misaki, Daigo Kogakuin Univ Grad Sch Engn Tokyo Japan Kogakuin Univ Dept Mech Syst Engn Tokyo Japan

ISBN: (纸本)9781665421973

The method of medication distribution differs from country to country, and because the standard method of medication distribution in Japan is the use of PTP (Press Through Pack) sheets, it is necessary to study a recognition method that does not depend on medication bottles. Therefore, we aimed to improve medication adherence by applying a method specific to Japanese medication. As a preliminary step, in this study, we defined "medication" as the action of taking out a medication from a PTP sheet instead of a bottle and pouring into 200 ml of water, measured using a simple device attached to the wrist with a triaxial acceleration sensor, and detected the presence of medication through an artificial neural network. The motions collected were "taking medication," "medicationlike motions," and "walking." As a result, the overall accuracy was 94.4%, and the accuracy was 91.7% for opening the PTP sheet and placing the medication into the mouth, and 91. 7% for the actions leading up to swallowing the medicine with water, all of which we prepared were recognized as medication. We were also able to show that 200 ml of water can be used to recognize the medication even when using a PTP sheet.

关键词： 3D accelerometer data Artificial neural network Medication adherence Wearable computing

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Multi-Relational Stacking Ensemble Recommender System Using Cinematic Experience

Multi-Relational Stacking Ensemble Recommender System Using ...

引用

IEEE international conference on big data and smart computing (bigcomp)

作者： Lee, CheonSol Han, DongHee Choi, SeungWoo Han, KeeJun Yi, MunYong Korea Adv Inst Sci & Technol Dept Ind & Syst Engn Daejeon South Korea Buzzvil Dept Data Engn Seoul South Korea Elect & Telecommun Res Inst Intelligent Convergence Res Lab Daejeon South Korea

ISBN: (纸本)9781665421973

With the advent of many movie content platforms, users face a flood of contents and consequent difficulties in selecting appropriate movie titles. Although much research has been done in developing effective recommender systems to provide personalized recommendations based on customers' past preferences and behaviors, not much attention has been paid to leveraging users' sentiments and emotions together. In this study, we built graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models. The sentiment and emotion information was extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies' meta-data and review data from the Rotten Tomatoes website. The study results show that the proposed models coupled with emotion and sentiment are superior over the conventional models. The results of this study support the possibility of using sentiment and emotion information together in relation to movie recommender system.

关键词： graph neural networks recommender system sentiment analysis emotion analysis BERT

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Distributed Classification Model of Streaming Tweets based on Dynamic Model Update

Distributed Classification Model of Streaming Tweets based o...

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IEEE international conference on big data and smart computing (bigcomp)

作者： Kim, Min-Seon Kwon, Hyuk-Yoon Seoul Natl Univ Sci & Technol Dept Ind Engn Seoul South Korea

ISBN: (纸本)9781665421973

In this study, we propose a distributed architecture that dynamically updates the model for classifying tweet streams generated in real time. Our architecture ingests data streams through Apache Kafka and classifies them based on Apache Spark Streaming. In order to dynamically reflect input stream changes into the classification model, we design the classification model that can be dynamically updated by updating the tokenizer and classifier for new tweet streams. The proposed architecture can provide effective classification for data streams due to the dynamic update and can efficiently process through parallel processing of distributed environments. Through experiments using cyberattack-related tweets, we show that our classification model gradually improves the classification accuracy from 0.8869 when the initial 50,000 tweets are used to 0.9094 when 200,000 tweets are accumulated by F1-score.

关键词： Event Classification Streaming Tweets data Ingestion Distributed Processing Dynamic Model Update

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An Efficient Distributed Spatiotemporal Index for Parallel Top-k Frequent Terms Query

An Efficient Distributed Spatiotemporal Index for Parallel T...

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IEEE international conference on big data and smart computing (bigcomp)

作者： Van Le, Hong Takasu, Atsuhiro Natl Inst Informat Tokyo Japan

ISBN: (纸本)9781665421973

As rich user-generated geotagged data, microblogs have been exploited in several data analytic contexts, e.g., popular topic trends, popular site detection, and geo-targeted recommendation. To support such analysis, we developed an efficient multidimensional index structure and parallel processing approaches for top-k frequent spatiotemporal terms query: a common analytic query on geotagged social data. Given a spatiotemporal range, the query aggregates the frequencies of terms among the social posts and identifies the most frequent terms in that range. The present work is different from studies that extract this information from stream data because we focus on large historical datasets. The key challenge is to improve query performance with minimum storage requirements. We propose a distributed index structure that transforms spatiotemporal coordinates into unique codes to generate rowkeys in key-value stores (KVSs) and balances the data distribution across distributed systems. Then, we utilize data localization to calculate sorted term lists (STLs) in parallel. To reduce input/output between KVSs and the client, we theoretically estimate the necessary length of STLs to calculate the top-k frequent terms and send only a part of the STLs to the client. Several experiments, on both real and artificial datasets, showed our approach to have both lower space requirements and better query performance than baseline approaches.

关键词： parallel processing top-k computing spatiotemporal database key-value store geotagged social data

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