کارابرن عزیز، مقالات isi بالاترین کیفیت ترجمه را دارند، ترجمه آنها کامل و دقیق می باشد (محتوای جداول و شکل های نیز ترجمه شده اند) و از بهترین مجلات isi انتخاب گردیده اند. همچنین تمامی ترجمه ها دارای ضمانت کیفیت بوده و در صورت عدم رضایت کاربر مبلغ عینا عودت داده خواهد شد.
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Big Data Architecture for Water Resources Management: A Systematic Mapping Study
معماری داده های بزرگ برای مدیریت منابع آب: یک مطالعه نقشه برداری سیستماتیک-2018
The combination of growth in demand for water, climate and hydrological gap, pushed the decision makers and water resource managers to search strategies for effective management of water resources. In this sense, the new generation of Business Intelligence technologies, known as Big Data, allows mass processing of complex data on a large scale. In recent years, several solutions have been proposed for management issues of water resources in general using Big Data. In this paper we provide an overview of proposed architectures features, serving as a starting point for further research
Keywords: Big Data; architecture; water; resources ;systematic mapping
Big Data Compliance for Innovative Clinical Models
مطابقت داده های بزرگ برای مدل های بالینی نوآورانه-2018
In the healthcare sector, information is the most important aspect, and the human body in particular is the major source of data production: as a result, the new challenge for world healthcare is to take advantage of these huge amounts of data de-structured among themselves. In order to benefit from this advantage, technology offers a solution called Big Data Analysis that allows the management of large amounts of data of a different nature and coming from different sources of a “computerized” healthcare, as there are considerable changes made by the input of digital technology in all major health areas. Clinical intelligence consists of all the analytical methods made possible through the use of computer tools, in all the processes and disciplines of extraction and transformation of crude clinical data into significant insights, new purposes and knowledge that provide greater clinical efficacy and best health pronouncements about past performance, current operations and future events. It can therefore be stated that clinical intelligence, through patient data analysis, will become a standard operating procedure that will address all aspects of care delivery. The purpose of this paper is to present clinical intelligence approaches through Data Mining and Process Mining, showing the differences between these two methodologies applied to perform “real process” extraction to be compared with the procedures in the corporate compliance template (the so called “Model 231”) by “conformance checking”.
Keywords: Big Data healthcare , Clinical intelligence , Data Mining , Process Mining , Business intelligence
An event-extraction approach for business analysis from online Chinese news
یک دیدگاه استخراج رویداد برای تحلیل کسب و کار از اخبار آنلاین چینی-2018
Extracting events from business news aids users to perceive market trends, be aware of competitors’ strategies, and to make valuable investment decisions. Prior research lacks event extraction in the area of business and event based business analysis, especially in Chinese language. We propose a novel business event-extraction approach integrating patterns, machine learning models and word embedding technology in deep learning, which is applied to extract events from online Chinese news. Word embedding and a semantic lexicon are utilized to extend an event trigger dictionary with high accuracy. Then the trigger features in the dictionary are introduced into a machine learning classification algorithm to implement more refined event-type recognition. Based on a scalable pattern tree, the event type that is discovered is used to find the best-suited pattern for extracting event elements from online news. Experimental results show the effectiveness of the proposed approach. In addition, empirical studies demonstrate the practical value of extracted events, especially in finding the relationships between news events and excess returns for stock, and analyzing industry trends based on events in China.
keywords: Business events |Business intelligence |Chinese text analytics |Event extraction |Explanatory econometrics |Machine learning models |Natural language processing |Online news |Patterns |Word embedding
Predicting the helpfulness of online product reviews: A multilingual approach
پیش بینی مفید بودن بازدیدهای آنلاین از محصول: یک دیدگاه چند زبانه-2018
Identifying helpful reviews from massive review data has been a hot topic in the past decade. While existing research on review helpfulness estimation and prediction is primarily sourced from English reviews, non-English reviews may also provide useful consumer opinion information and should not be neglected. In this study, we propose a review helpfulness prediction framework that processes and uses multilingual sources of reviews to generate relevant business insights. Adopting a design science research approach, we design, implement, evaluate and deliver an IT artifact (i.e., our framework) that predicts the helpfulness of a review and accounts for non-English reviews. Our evaluations suggest that we achieve better performance on review helpfulness prediction and classification by including the variables generated by our instantiated multilingual system. By demonstrating the feasibility of our proposed framework for multilingual business intelligence applications, we contribute to the literature on business intelligence and provide important practical implications to practitioners.
keywords: Word-of-mouth |Product reviews |Multilingual reviews |Review helpfulness |Prediction
Human resources for Big Data professions: A systematic classification of job roles and required skill sets
مدیریت منابع انسانی برای تخصص های داده های بزرگ: یک دسته بندی سیستماتیک نقش های شغلی و سری مهارتهای مورد نیاز-2018
The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace.
keywords: Big Data |Business intelligence |Human resources management |Machine learning |Topic modeling
Big Data Compliance for Innovative Clinical Models
پذیرش داده های بزرگ برای مدل های بالینی نوآورانه-2018
Article history:Available online xxxxKeywords:Big Data healthcare Clinical intelligence Data Mining Process MiningBusiness intelligenceIn the healthcare sector, information is the most important aspect, and the human body in particular is the major source of data production: as a result, the new challenge for world healthcare is to take advantage of these huge amounts of data de-structured among themselves. In order to beneﬁt from this advantage, technology offers a solution called Big Data Analysis that allows the management of large amounts of data of a different nature and coming from different sources of a “computerized” healthcare, as there are considerable changes made by the input of digital technology in all major health areas.Clinical intelligence consists of all the analytical methods made possible through the use of computer tools, in all the processes and disciplines of extraction and transformation of crude clinical data into signiﬁcant insights, new purposes and knowledge that provide greater clinical eﬃcacy and best health pronouncements about past performance, current operations and future events. It can therefore be stated that clinical intelligence, through patient data analysis, will become a standard operating procedure that will address all aspects of care delivery.The purpose of this paper is to present clinical intelligence approaches through Data Mining and Process Mining, showing the differences between these two methodologies applied to perform “real process” extraction to be compared with the procedures in the corporate compliance template (the so called “Model 231”) by “conformance checking”. 2018 Elsevier Inc. All rights reserved.
kywords: Big Data healthcare | Clinical intelligence | Data Mining | Process Mining | Business intelligence
هوش تجاری و یادگیری سازمانی: یک بررسی از فرایندهای ایجاد ارزش
سال انتشار: 2017 - تعداد صفحات فایل pdf انگلیسی: 19 - تعداد صفحات فایل doc فارسی: 71
این تحقیق با هدف ایجاد پل و پر کردن شکاف موجود میان تحقیقات تثبیتشده در حوزهی ایجاد ارزش فناوری اطلاعات (IT) و مطالعات اورژانسی هوش مصنوعی (BI)، یک مدل از ایجاد ارزش BI را توسعه داده و تست میکند، که این مدل دستی در هر دو جریان از تحقیقات فوق دارد. به منظور فرضیهسازی در مورد مسیرهایی که داراییها و قابلیتهای BI، ارزش کسبوکاری ایجاد میکنند، تحلیلی بر روی دید مبتنی بر منبع و مفهومیسازی یادگیری سازمانی، طراحی میشود. ابتدا مدل تحقیقی در یک تحلیل اکتشافی بر روی دادههای جمعآوری شده از طریق مصاحبه در سه شرکت، مورد بررسی قرار گرفته و سپس این مدل در یک تحلیل تاییدی بر روی دادههای جمعآوری شده از طریق یک مطالعهی مروری، تست میشود.
کلمات کلیدی: هوش تجاری (BI) | ارزش کسبوکار | دید مبتنی بر منبع (RBV) | یادگیری سازمانی | اکتشاف و بهرهبرداری
|مقاله ترجمه شده|
Retail supply chain management practices in India: A business intelligence perspective
شیوه های مدیریت زنجیره تامین در هند: چشم انداز هوش تجاری-2017
The study surveyed executives of a major food retailer in India and explored their perspectives on supply chain management practices, competitive advantage and firm performance; to assess the importance accorded to application of business intelligence (BI) in their operations. Nine dimensions for SCM practices and four dimensions for competitive advantage are identified which are found to strongly relate to each other. The dimensions of SCM also strongly relate to firm performance. Though information sharing with suppliers and their inclusion in strategic decision-making emerge as key dimensions of SCM, their impact on competitive advantage is perceived to be insignificant by retailers.
Keywords:Supply chain management|Business intelligence|Emerging market|Food retailer|Competitive advantage|Firm performance
Investigating the role of the rating prediction task in granularity-based group recommender systems and big data scenarios
بررسی نقش کار پیش بینی ارزیابی در سیستم های پیشنهاد دهنده گروه مبتنی بر دانه دانه ها و سناریوهای داده های بزرگ-2017
Nowadays, one important issue for companies is the efficient dealing of the big data problem, which means that their business intelligence has to manage huge amounts of data. An interesting case in point is flyers distribution. Research and market figures prove that the distribution of advertising flyers still represents a valuable tool to attract potential customers to a company. It goes without saying that including personalized content in a company’s flyer is more likely to yield better results than offering the same flyer to all potential clients. However, producing personalized flyers would imply unaffordable costs for a company. An efficient trade-off solution between accuracy and costs could be to define a maximum number of different flyers addressing different groups of users interested in their content. In order to systematically support this and similar trade-off solutions, we propose a novel type of group recommendations, which is able to detect a number of groups of end-users equal to the number of recommendation lists (e.g., flyers) that can be produced (i.e., the granularity with which the system can operate). Moreover, it can provide suggestions to the detected specific groups of users. In particular, we focus on the rating prediction for those items users do not evaluate. Indeed, rating prediction represents the main task that a recommender system is asked to perform and it becomes even more central if included into a group recommender system, since the predictions might be built for each user or for each group. Our approach also gives the possibility to efficiently manage the curse of the dimensionality phenomena caused by the sparsity of the ratings arising from big data handling. We present four granularity-based group recommender systems using different rating prediction algorithms and architectures. These systems employ the same algorithms to carry out other tasks (i.e., those that do not predict the ratings) and this allows us to evaluate which rating prediction approach is the most effective in terms of accuracy. Experiments on two real-world datasets show that, unlike group predictions, single user predictions can lead to improvements in the recommendation accuracy and the dealing of the curse of the dimensionality phenomena.
Keywords: Group recommendation | Clustering | Rating prediction | Big data
Data governance case at KrauseMcMahon LLP in an era of self-service BI and Big Data
مورد مدیریت داده در KrauseMcMahon LLP در عصر خود سرویس هوش تجاری و داده های بزرگ-2017
This case increases your understanding of data governance in an era of sophisticated ana lytics and Big Data where corporate data integrity and data quality may be at risk. KrauseMcMahon, a large certified public accounting and business consulting firm, faces a tradeoff of increasing control of the company’s data assets versus unleashing end user innovation due to the proliferation of self-service business intelligence tools. You are required to analyze the issues in the case from organizational, financial, and technical per spectives to propose alternatives the organization should consider and make specific rec ommendations on how the company should proceed. By completing this case, you will demonstrate cross-disciplinary abilities related to foundational business, accounting, and broad management competencies. By addressing such competencies, the case requires your use of accounting, MIS, and upper-level business skills.
Keywords:Big Data|Data governance|Self-service business intelligence