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Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data
ViSiBiD: مدل های یادگیری برای کشف زودرس و پیش بینی زمان واقعی از حوادث بالینی شدید با استفاده از علائم حیاتی به عنوان داده های بزرگ-2017
The advance in wearable and wireless sensors technology have made it possible to monitor multiple vital signs (e.g. heart rate, blood pressure) of a patient anytime, anywhere. Vital signs are an essential part of daily monitoring and disease prevention. When multiple vital sign data from many patients are accumulated for a long period they evolve into big data. The objective of this study is to build a prognostic model, ViSiBiD, that can accurately identify dangerous clinical events of a home-monitoring patient in advance using knowledge learned from the patterns of multiple vital signs from a large number of similar patients. We developed an innovative technique that amalgamates existing data mining methods with smartly extracted features from vital sign correlations, and demonstrated its effectiveness on cloud platforms through comparative evaluations that showed its potential to become a new tool for predictive healthcare. Four clinical events are identified from 4893 patient records in publicly available databases where six bio-signals deviate from normality and different features are extracted prior to 1–2 h from 10 to 30 min observed data of those events. Known data mining algorithms along with some MapReduce implementations have been used for learning on a cloud platform. The best accuracy (95.85%) was obtained through a Random Forest classifier using all features. The encouraging learning performance using hybrid feature space proves the existence of discriminatory patterns in vital sign big data can identify severe clinical danger well ahead of time.
Keywords: Big data | Vital sign | Cloud computing | Correlations | Knowledge discovery | Data mining
Visualizing the knowledge structure and evolution of big data research in healthcare informatics
بصری سازی ساختار دانش و تکامل تحقیقات داده های بزرگ در انفورماتیک بهداشتی-2017
Background: In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field. Methods: To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers’ production trends in the field and the trend of each paper’s co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future. Results: By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People’s Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing). Conclusion: This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions.
Keywords: Big data | Healthcare informatics | Bibliometrics | Knowledge structure | Knowledge management
Food safety pre-warning system based on data mining for a sustainable food supply chain
سیستم ایمنی غذا بر اساس داده کاوی برای یک زنجیره تامین مواد غذایی پایدار-2017
In recent years, the food safety incidents happened frequently in china, and then the problems related to food quality and safety have attracted more and more social attention. Considering the concern with regard to quality sustainability in food supply chain, many companies have developed a real time data monitoring system to ensure products quality in the supply chain network. In this paper, we proposed a food safety pre-warning system, adopting association rule mining and Internet of Things technology, to timely monitor all the detection data of the whole supply chain and automatically pre-warn. The aim of pre-warning system is to help managers in food manufacturing firm to find food safety risk in advance, and to give some decision support information to maintain the quality and safety of food products. A case study of a dairy producer was conducted, and the results showed that the proposed pre-warning system can effectively identify safety risks and accurately determine whether a warning should be issued, depending on the expert analysis when an abnormality is detected by the system. In addition, impli cations of the proposed approach were discussed, and suggestions for future work were outlined.
Keywords:Food safety | Food supply chain | Pre-warning system | Association rule
داده کاوی iCLIC و کارگاه به اشتراک گذاری داده ها: داده کاوی حال و آینده و به اشتراک گذاری داده ها در اتحادیه اروپا
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 27
همانگونه که در محوطه دانشگاه هایفیلد دانشگاه ساوت هامپتون و به میزبانی iCLIC برگزار شد، هسته میان رشته ای در قانون، اینترنت و فرهنگ، داده کاوی و کارگاه اشتراک گذاری داده ها، شرکت کنندگان و سخنرانانی از صنعت، دولت، دانشگاه و طیف وسیعی از عموم رشته ها را گرد هم آورد. این کارگاه دو جلسه را در برمی گرفت، هر یک شامل یک سخنرانی اصلی و یک گروه ارتباطی می شد. اولین جلسه توسط النورا روساتی ریاست شد و به حقوق کپی رایت و پایگاه داده ها، داده کاوی اطلاعات و به اشتراک گذاری داده ها پرداخته شد. جلسه دوم، به ریاست سوفی اِستالا بوردیلون در حفاظت از داده ها، داده کاوی و به اشتراک گذاری داده ها متمرکز شده بود. گزارش زیر هر دو جلسه را پوشش می دهد و بحث گروهی را در اینباره صورت داده و به پرسش و پاسخ می پردازد.
کلمات کلیدی: داده کاوی | کپی رایت | حقوق پایگاه داده | اشتراک گذاری تاریخ | حفاظت از داده ها | حقوق مالکیت معنوی | مقررات حفاظت داده های عمومی | (EU-GDPR) | پاکسازی وب
|مقاله ترجمه شده|
ClowdFlows: Online workflows for distributed big data mining
ClowdFlows: گردش کار آنلاین برای کاوش داده های بزرگ توزیع شده-2017
The paper presents a platform for distributed computing, developed using the latest software technologies and computing paradigms to enable big data mining. The platform, called ClowdFlows, is implemented as a cloud-based web application with a graphical user interface which supports the construction and execution of data mining workflows, including web services used as workflow components. As a web application, the ClowdFlows platform poses no software requirements and can be used from any modern browser, including mobile devices. The constructed workflows can be declared either as private or public, which enables sharing the developed solutions, data and results on the web and in scientific publications. The server-side software of ClowdFlows can be multiplied and distributed to any number of computing nodes. From a developer’s perspective the platform is easy to extend and supports distributed development with packages. The paper focuses on big data processing in the batch and real-time processing mode. Big data analytics is provided through several algorithms, including novel ensemble techniques, implemented using the map-reduce paradigm and a special stream mining module for continuous parallel workflow execution. The batch mode and real-time processing mode are demonstrated with practical use cases. Performance analysis shows the benefit of using all available data for learning in distributed mode compared to using only subsets of data in non-distributed mode. The ability of ClowdFlows to handle big data sets and its nearly perfect linear speedup is demonstrated.
Keywords:Data mining platform|Cloud computing|Scientific workflows|Batch processing|Map-reduce|Big data
A Hybrid Approach for Big Data Outlier Detection from Electric Power SCADA System
روش ترکیبی برای تشخیص پرت داده های بزرگ سیستم برق SCADA-2017
Supervisory control and data acquisition (SCADA) databases have three main features that identify them as big data systems: volume, variety and velocity. SCADAs are extremely important for the safety and secure operation of modern power system and provide essential online information about the power system state to system operators. A current research challenge is to efficiently process this big data, which involves real-time measurements of hundreds of thousands of heterogeneous electrical power system physical measurements. Among the foreseen automation tasks, outlier detection is one of the most important data mining techniques for power systems. However, like others data mining techniques, traditional outlier detection fails when dealing with problems in which the volume and dimensionality of data are as high as the ones observed in a SCADA. This work aims at circumventing these restrictions by presenting a methodology for dealing with SCADA big data that consists of a pre-processing algorithm and hybrid approach outlier detectors. The hybrid approach is assessed using real data from a Brazilian utility company. The results show that the proposed methodology is capable of identifying outliers correlated with important events that affect the system.
Keywords: Outlier detection | high dimensionality | massive datasets | SCADA | electric power systems.
Exploiting Mobile Big Data: Sources, Features, and Applications
بهره برداری از داده های بزرگ سیار: منابع، ویژگی ها و برنامه های کاربردی-2017
The worldwide rollout of 4G LTE mobile com munication networks has accelerated the prolif eration of the mobile Internet and spurred a new wave of mobile applications on smartphones. This new wave has provided mobile operators an enormous opportunity to collect a huge amount of data to monitor the technical and transaction al aspects of their networks. Recent research on mobile big data mining have shown its great potential for diverse purposes ranging from improving traffic management, enabling personal and contextual services, to monitoring city dynam ics and so on. The mobile big data research has a multi-disciplinary nature that demands distinct knowledge from mobile communications, signal processing, and data mining. The research field of mobile big data has emerged quickly in recent years, but is somewhat fragmented. This article aims to provide an integrated picture of this emerging field to bridge multiple disciplines and hopefully to inspire future research.
kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data
kNN-IS: طراحی مبتنی بر اسپارک تکراری از K- نزدیکترین همسایه طبقه بندی برای داده های بزرگ-2017
The k-Nearest Neighbors classifier is a simple yet effective widely renowned method in data mining. The actual application of this model in the big data domain is not feasible due to time and memory restric tions. Several distributed alternatives based on MapReduce have been proposed to enable this method to handle large-scale data. However, their performance can be further improved with new designs that fit with newly arising technologies. In this work we provide a new solution to perform an exact k-nearest neighbor classification based on Spark. We take advantage of its in-memory operations to classify big amounts of unseen cases against a big training dataset. The map phase computes the k-nearest neighbors in different training data splits. Afterwards, multiple reducers process the definitive neighbors from the list obtained in the map phase. The key point of this proposal lies on the management of the test set, keeping it in memory when possi ble. Otherwise, it is split into a minimum number of pieces, applying a MapReduce per chunk, using the caching skills of Spark to reuse the previously partitioned training set. In our experiments we study the differences between Hadoop and Spark implementations with datasets up to 11 million instances, show ing the scaling-up capabilities of the proposed approach. As a result of this work an open-source Spark package is available.
Keywords:K-nearest neighbors|Big data|Apache Hadoop|Apache Spark|MapReduce
A Biological Immune System (BIS) inspired Mobile Agent Platform (MAP) security architecture
یک سیستم ایمنی زیستی (BIS) معماری امنیت تلفن همراه عامل (MAP) را الهام بخشید-2017
Article history:Received 22 June 2016Revised 21 October 2016Accepted 31 October 2016Available online 9 November 2016Keywords:Malicious mobile agent Nonce-based authentication Pattern matchingN-gram feature extraction Feature selectionK-nearest neighbor classiﬁcationThe proliferation of malicious entities in the distributed environment poses various serious threats to the protection of Mobile Agent Platform (MAP). Numerous researches have been proposed to ward off the inherent security risks, though these solutions are not enough to identify and remove all the vulnerabil- ities. In this paper, a self-adaptive IV-Phase MAP Security Architecture is proposed, which is inspired by the Biological Immune System, with the prime objective of detecting unknown malicious mobile agents. In this context, data mining methods are studied for the detection of unknown malicious executable. In particular, Boyer Moore pattern matching algorithm and N-gram feature analysis of mobile agent using a k-Nearest Neighbor Classiﬁer, facilitate the discovery of known and unknown malicious content from incoming mobile agent in the proposed architecture, and protects against the Man In The Middle (MITM) attack, the Masquerading Attack, the Replay attack, the Repudiation attack and the Unauthorized Access Attack. The architecture is designed and implemented in IBM Aglets. A comprehensive 5-fold cross vali- dation scheme on a large collection of malicious and non-malicious ﬁles is performed while performing Classiﬁcation technique involving Feature Selection Method. The propitious experimental outcomes ex- press that the performance (time and security) and accuracy of proposed architecture outperform the earlier known related schemes and makes the proposed architecture suitable for MAP protection in the Mobile Agent Environment (MAE). Above all, these ﬁndings exhibit wide-ranging newness, since the con- cept of machine learning has never been employed so far in the sphere of Mobile Agents System (MAS). Hence the proposed work is likely to be of great interest to the researchers who particularly deal with MAS security.© 2016 Elsevier Ltd. All rights reserved.
Keywords: Malicious mobile agent | Nonce-based authentication | Pattern matching | N-gram feature extraction | Feature selection | K-nearest neighbor classification