با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Chapter 5 - Managing and Marketing Resources
فصل 5 - مدیریت و منابع بازاریابی-2017 Libraries began to hear about the need to do marketing in the 1980s
and 1990s. It was a period that followed budget retrenchments of the
1970s, and a time when librarians began to realize that libraries were
not universally regarded as something positive and valuable, organiza
tions playing an essential function, and needing resources. Librarians
awoke to the fact that funding agencies, governing bodies, elected
officials, and institutional administrators did not necessarily under
stand what libraries and librarians do, and what they can provide in
the way of access to resources and instruction in using them. In the
mid-to-late 1990s, the emergence of the Internet had two competing
effects: it allowed more-sophisticated and varied approaches to mar
keting, and it presented a new marketing challenge: the idea that
“everything is on the Internet” and, moreover, that it is “free on the
Internet.”
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مقاله انگلیسی |
32 |
QDrill: Query-Based Distributed Consumable Analytics for Big Data
QDrill: تجزیه و تحلیل مصرفی توزیع شده مبتنی بر پرس و جو برای داده های بزرگ-2016 Consumable analytics attempt to address the
shortage of skilled data analysts in many organizations by
offering analytic functionality in a form more familiar to inhouse expertise. Providing consumable analytics for Big Data
faces three main challenges. The first challenge is making the
analytics algorithms run in a distributed fashion in order to
analyze Big Data in a timely manner. The second challenge is
providing an easy interface to allow in-house expertise to run
these algorithms in a distributed fashion while minimizing the
learning cycle and existing code rewrites. The third challenge is
running the analytics on data of different formats stored on
heterogeneous data stores.
In this paper, we address these challenges in the proposed
QDrill. We introduce the Analytics Adaptor extension for
Apache Drill, a schema-free SQL query engine for nonrelational storage. The Analytics Adaptor introduces the
Distributed Analytics Query Language for invoking data
mining algorithms from within the Drill standard SQL query
statements. The adaptor allows using any sequential singlenode data mining library (e.g. WEKA) and makes its
algorithms run in a distributed fashion without having to
rewrite them. We evaluate QDrill against Apache Mahout. The
evaluation shows that QDrill outperforms Mahout in Updatable model training and scoring phase while almost
keeping the same performance for Non-Updatable model training. QDrill is more scalable and offers an easier interface,
no storage overhead and the whole algorithms repository of WEKA, with the ability to extend to use algorithms from other
data mining libraries.
Keywords: Big Data| Analytics | SQL | Data Mining | Distributed | Apache Drill | WEKA |
مقاله انگلیسی |
33 |
Digital library interoperability at high level of abstraction
قابلیت همکاری کتابخانه های دیجیتال در سطح بالایی از انتزاع-2016 Article history:Received 14 July 2015 Received in revised form 4 September 2015Accepted 16 September 2015Available online 3 October 2015Keywords:Digital library Foundational models 5S modelDELOS Reference Model Interoperability OntologyDigital Library (DL) are the main conduits for accessing our cultural heritage and they have to address the requirements and needs of very diverse memory institutions, namely Libraries, Archives and Museums (LAM). Therefore, the interoperability among the Digital Library System (DLS) which manage the digital resources of these institutions is a key concern in the field.DLS are rooted in two foundational models of what a digital library is and how it should work, namely the DELOS Reference Model and the Streams, Structures, Spaces, Scenarios, Societies (5S) model. Unfortunately these two models are not exploited enough to improve interoperability among systems.To this end, we express these foundational models by means of ontologies which exploit the methods and technologies of Semantic Web and Linked Data. Moreover, we link the proposed ontologies for the foundational models to those currently used for publishing cultural heritage data in order to maximize interoperability.We design an ontology which allows us to model and map the high level concepts of both the 5S model and the DELOS Reference Model. We provide detailed ontologies for all the domains of such models, namely the user, content, functionality, quality, policy and architectural component domains in order to make available a working tool for making DLS interoperate together at a high level of abstraction. Finally, we provide a concrete use case about digital annotation of illuminated manuscripts to show how to apply the proposed ontologies and illustrate the achieved interoperability between the 5S and DELOS Reference models.© 2015 Elsevier B.V. All rights reserved.
Digital library | Foundational models | 5S model | DELOS Reference Model | Interoperability | Ontology |
مقاله انگلیسی |
34 |
Social big data: Recent achievements and new challenges
داده های بزرگ اجتماعی: دستاوردهای اخیر و چالش های جدید-2016 Article history:Available online 28 August 2015Keywords: Big data Data mining Social mediaSocial networksSocial-based frameworks and applicationsBig data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and ma- chine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combina- tion of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fu- sion from social media and of the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media and big data paradigms.© 2015 Elsevier B.V. All rights reserved.
Social big data | Recent achievements | new challenges |
مقاله انگلیسی |
35 |
SIMD parallel MCMC sampling with applications for big-data Bayesian analytics
SIMD نمونه گیری MCMC موازی با برنامه های کاربردی برای تجزیه و تحلیل بیزی داده های بزرگ-2015 Computational intensity and sequential nature of estimation techniques for Bayesian
methods in statistics and machine learning, combined with their increasing applications
for big data analytics, necessitate both the identification of potential opportunities to
parallelize techniques such as Monte Carlo Markov Chain (MCMC) sampling, and the
development of general strategies for mapping such parallel algorithms to modern CPUs
in order to elicit the performance up the compute-based and/or memory-based hardware
limits. Two opportunities for Single-Instruction Multiple-Data (SIMD) parallelization of
MCMC sampling for probabilistic graphical models are presented. In exchangeable models
with many observations such as Bayesian Generalized Linear Models (GLMs), child-node
contributions to the conditional posterior of each node can be calculated concurrently.
In undirected graphs with discrete-value nodes, concurrent sampling of conditionallyindependent nodes can be transformed into a SIMD form. High-performance libraries
with multi-threading and vectorization capabilities can be readily applied to such SIMD
opportunities to gain decent speedup, while a series of high-level source-code and runtime
modifications provide further performance boost by reducing parallelization overhead
and increasing data locality for Non-Uniform Memory Access architectures. For big-data
Bayesian GLM graphs, the end-result is a routine for evaluating the conditional posterior
and its gradient vector that is 5 times faster than a naive implementation using (built-in)
multi-threaded Intel MKL BLAS, and reaches within the striking distance of the memorybandwidth-induced hardware limit. Using multi-threading for cache-friendly, fine-grained
parallelization can outperform coarse-grained alternatives which are often less cachefriendly, a likely scenario in modern predictive analytics workflow such as Hierarchical
Bayesian GLM, variable selection, and ensemble regression and classification. The proposed
optimization strategies improve the scaling of performance with number of cores and
width of vector units (applicable to many-core SIMD processors such as Intel Xeon Phi
and Graphic Processing Units), resulting in cost-effectiveness, energy efficiency (‘green
computing’), and higher speed on multi-core x86 processors.
Keywords:
GPU
Hierarchical Bayesian
Intel Xeon Phi
Logistic regression
OpenMP
Vectorization |
مقاله انگلیسی |
36 |
Literature Review of Data Mining Applications in Academic Libraries
بررسی ادبیات نرم افزار داده کاوی در کتابخانه های دانشگاهی-2015 This article provides a comprehensive literature review and classification method for data mining techniques applied to academic libraries. To achieve this, forty-one practical contributions over the period 1998–2014 were identified and reviewed for their direct relevance. Each article was categorized according to the main data mining functions: clustering, association, classification, and regression; and their application in the four main library aspects: services, quality, collection, and usage behavior. Findings indicate that both collection and usage behavior analyses have received most of the research attention, especially related to collection development and usability of websites and online services respectively. Furthermore, classification and regression models are the two most commonly used data mining functions applied in library settings.Additionally, results indicate that the top 6 journals of articles published on the application of data mining techniques in academic libraries are: College and Research Libraries, Journal of Academic Librarianship, Informa- tion Processing and Management, Library Hi Tech, International Journal of Knowledge, Culture and Change Management, and The Electronic Library. Scopus is the multidisciplinary database that provides the best coverage of journal articles identified. To our knowledge, this study represents the first systematic, identifiable and comprehensive academic literature review of data mining techniques applied to academic libraries.© 2015 Elsevier Inc. All rights reserved.
Keywords: Data mining | Bibliomining | Literature review | Academic libraries |
مقاله انگلیسی |
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Feature selection based on closed frequent itemset mining: A case study on SAGE data classification
انتخاب ویژگی بر اساس کاوش مجموعه اقلام مکرر مسدود: مطالعه موردی در طبقه بندی داده های SAGE-2015 Cancer is curable if it can be detected early. One way to detect cancer is by analyzing the change in expression of genes in the suspected tissue. Serial analysis of gene expression (SAGE) is a sequencing technique used for measuring the expression levels of genes. Cancer detection problem can be posed as binary classification problem like whether a tissue is cancerous or normal. SAGE libraries contain expression levels of thousands of genes which are the features. It is impossible to consider all these features for classification and also the general feature selection algorithms are not efficient with this data. In this paper, closed frequent itemset mining is proposed as a feature selection technique for identifying a small set of features which can distinguish the two classes efficiently. The performance of the proposed technique is evaluated on SAGE data related to breast tissue and a group of 26 genes are selected as best features. Two well known classifiers, extreme learning machine (ELM) and support vector machine (SVM), are used to evaluate the effectiveness of the selected features in classification and found that the proposed method works well with these classifiers.& 2014 Elsevier B.V. All rights reserved.
Keywords: Closed frequent itemset mining | Feature selection | Serial analysis of gene expression | Extreme learning machine | Support vector machine | Classification |
مقاله انگلیسی |
38 |
هوشمندی رقابتی: ابزار برای عملکرد موثر شغلی در کتابخانه های دانشگاهی
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 22 دراین مقاله هوشمندی رقابتی مورد بحث قرار گرفته است که ابزاری برای عملکرد موثر شغلی در کتابخانه های دانشگاهی می باشد. این مقاله روش توصیفی پژوهش را به کار می گیرد تا استفاده از هوشمندی رقابتی را برای خدمات ارائه شده توسط کتابخانه دانشگاهی در هر موسسه آموزش عالی بیان کند. در حال حاضر، کتابخانه ها و مراکز اطلاع رسانی به طورمداوم خدمات نوآورانه و خلاقانه را توسعه می دهند تا با جامعه به سرعت در حال تغییر همگام باشند. تحولات فناوری اطلاعات و ارتباطات، به ویژه کسانی که دسترسی آسان به اطلاعات را در وب فراهم می اورند ، بطور قابل توجهی انتظارات کاربران کتابخانه را افزایش داده اند این انتظارات داشتن همان سرعت، وسعت و جامعیت در خدمات اطلاعات ارائه شده توسط کتابخانه است. از این رو، نیاز فوری برای معرفی هوشمندی رقابتی به کتابخانه و حرفه علم اطلاعات وجود دارد، تا خدمات ارائه شده به مشتریان خود را غنی سازد. این مقاله همچنین برخی از خدمات ارائه شده توسط کتابخانه های دانشگاهی را مورد بحث قرار می دهد و چگونگی کاربرد هوشمندی رقابتی را برای برخی از وظایف اساسی انجام شده توسط کتابداران به منظور هم سو بودن با روند فعلی در این حرفه ،برجسته می سازد.این مقاله با کمک علم اطلاعات و کتابداری (LIS) برای شناسایی و استفاده از انواع منابع اطلاعاتی غیر سنتی مانند هوشمندی رقابتی به نتیجه گیری می پردازد وکتابخانه های دانشگاهی را قادر به بیرون راندن رقبای خود از میدان نموده و کاربران کتابخانه را برای براوردن نیازهای اطلاعاتی خود درخدمات ارائه شده توسط کتابخانه وادار به تجدید علاقه می کند.
واژه های کلیدی: هوشمندی رقابتی | کتابخانه های دانشگاهی | عملکرد شغلی | حرفه های اطلاعات. |
مقاله ترجمه شده |
39 |
هوش رقابتی: ابزاری برای عملکرد شغلی موثر در کتابخانه های دانشگاهی
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 21 دراین مقاله هوش رقابتی مورد بحث قرار گرفته است که ابزاری برای عملکرد موثر شغلی در کتابخانه های دانشگاهی می باشد. این مقاله روش توصیفی پژوهش را به کار می گیرد تا استفاده از هوش رقابتی را برای خدمات ارائه شده توسط کتابخانه دانشگاهی در هر موسسه آموزش عالی بیان کند. در حال حاضر، کتابخانه ها و مراکز اطلاع رسانی به طورمداوم خدمات نوآورانه و خلاقانه را توسعه می دهند تا با جامعه به سرعت در حال تغییر همگام باشند. تحولات فناوری اطلاعات و ارتباطات، به ویژه کسانی که دسترسی آسان به اطلاعات را در وب فراهم می اورند ، بطور قابل توجهی انتظارات کاربران کتابخانه را افزایش داده اند این انتظارات داشتن همان سرعت، وسعت و جامعیت در خدمات اطلاعات ارائه شده توسط کتابخانه است. از این رو، نیاز فوری برای معرفی هوش رقابتی به کتابخانه و حرفه علم اطلاعات وجود دارد، تا خدمات ارائه شده به مشتریان خود را غنی سازد. این مقاله همچنین برخی از خدمات ارائه شده توسط کتابخانه های دانشگاهی را مورد بحث قرار می دهد و چگونگی کاربرد هوش رقابتی را برای برخی از وظایف اساسی انجام شده توسط کتابداران به منظور هم سو بودن با روند فعلی در این حرفه ،برجسته می سازد.این مقاله با کمک علم اطلاعات و کتابداری (LIS) برای شناسایی و استفاده از انواع منابع اطلاعاتی غیر سنتی مانند هوش رقابتی به نتیجه گیری می پردازد وکتابخانه های دانشگاهی را قادر به بیرون راندن رقبای خود از میدان نموده و کاربران کتابخانه را برای براوردن نیازهای اطلاعاتی خود درخدمات ارائه شده توسط کتابخانه وادار به تجدید علاقه می کند.
کلمات کلیدی: هوش رقابتی | کتابخانه های دانشگاهی | عملکرد شغلی | حرفه های اطلاعات. |
مقاله ترجمه شده |