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ردیف | عنوان | نوع |
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1 |
Efficient techniques for time-constrained information dissemination using location-based social networks
تکنیک های کارآمد برای زمان محدود انتشار اطلاعات محرمانه با استفاده از شبکه های اجتماعی مبتنی بر مکان-2017 Social networks have undergone an explosive growth in recent years. They constitute a
central part of users everyday lives as they are used as major tools for the spread of
information, ideas and notifications among the members of the network. In this work we
investigate the use of location-based social networks as a medium of emergency notifica
tion, for efficient dissemination of emergency information among members of the social
network under time constraints. Our objective is the following: given a location-based
social network comprising a number of mobile users, the social relationships among the
users, the set of recipients, and the corresponding timeliness requirements, our goal is to
select an appropriate subset of users so that the spread of information is maximized, time
constraints are satisfied and costs are considered. We propose LATITuDE, our system that
investigates the interactions among the members of the social network to infer their social
relationships, and develop scalable dissemination mechanisms that select the most efficient
set of users to initiate the dissemination process in order to maximize the information reach
among the appropriate receivers within a time window. Our detailed experimental results
illustrate that our approach is practical, effectively addresses the problem of informing the
appropriate set of users within a deadline when an emergency event occurs, uses a small
number of messages, and consistently outperforms its competitors.
Keywords: Distributed systems | Social networks | Information dissemination |
مقاله انگلیسی |
2 |
MidHDC: Advanced topics on middleware services for heterogeneous distributed computing: Part 2✩
MidHDC: Advanced topics on middleware services for heterogeneous distributed computing: Part 2-2017 Currently distributes systems support different computing paradigms like Cluster Computing, Grid
Computing, Peer-to-Peer Computing, and Cloud Computing all involving elements of heterogeneity. These
computing distributed systems are often characterized by a variety of resources that may or may not be
coupled with specific platforms or environments. All these topics challenge today researchers, due to the
strong dynamic behavior of the user communities and of resource collections they use.
The second part of this special issue presents advances in allocation algorithms, service selection, VM
consolidation and mobility policies, scheduling multiple virtual environments and scientific workflows,
optimization in scheduling process, energy-aware scheduling models, failure Recovery in shared Big
Data processing systems, distributed transaction processing middleware, data storage, trust evaluation,
information diffusion, mobile systems, integration of robots in Cloud systems.
Keywords: Middleware services | Resource management | Mobile computing | Cloud computing | HPC | Heterogeneous distributed systems |
مقاله انگلیسی |
3 |
Improving the robustness and performance of parallel joins over distributed systems
بهبود کارایی و کارایی اتصالات موازی در سیستم های توزیع شده-2017 High-performance data processing systems typically utilize numerous servers with large amounts of
memory. An essential operation in such environment is the parallel join, the performance of which
is critical for data intensive operations. In many real-world workloads, data skew is omnipresent.
Techniques that do not cater for the possibility of data skew often suffer from performance failures
and memory problems. State-of-the-art methods designed to handle data skew propose new ways to
distribute computation that avoid hotspots. However, this comes at the expense of global collection
of statistics, redundant computation, duplication of data or increased network communication. In this
light, performance could be further improved by removing the dependency on global skew knowledge
and broadcasting. In this paper, we propose a new method called PRPQ (partial redistribution & partial
query), with targets for efficient and robust joins with large datasets over high performance clusters.
We present the detailed implementation of our approach and compare its performance with current
implementations. The experimental results demonstrate that the proposed algorithm is scalable and
robust and can also outperform the state-of-the-art approach with less network communication, figures
that confirm our theoretical analysis.
Keywords: Parallel joins | Data skew | Robust | High performance computing |
مقاله انگلیسی |
4 |
Persisting big-data: The NoSQL landscape
تداوم داده های بزرگ: چشم انداز NoSQL-2017 The growing popularity of massively accessed Web applications that store and analyze
large amounts of data, being Facebook, Twitter and Google Search some prominent
examples of such applications, have posed new requirements that greatly challenge tra
ditional RDBMS. In response to this reality, a new way of creating and manipulating data
stores, known as NoSQL databases, has arisen. This paper reviews implementations of
NoSQL databases in order to provide an understanding of current tools and their uses.
First, NoSQL databases are compared with traditional RDBMS and important concepts are
explained. Only databases allowing to persist data and distribute them along different
computing nodes are within the scope of this review. Moreover, NoSQL databases are
divided into different types: Key-Value, Wide-Column, Document-oriented and Graph
oriented. In each case, a comparison of available databases is carried out based on their
most important features.
Keywords:NoSQL databases|Relational databases|Distributed systems|Database persistence|Database distribution|Big data |
مقاله انگلیسی |
5 |
A Multi-Agent Case-Based Reasoning Architecture for Phishing Detection
معماری استنتاجی مبتنی بر مورد چند عاملی برای تشخیص سرقت اطلاعات -2017 Security threats are becoming very sophisticated and pervasive everywhere. Phishing threats in particular has a changeable nature
and short life cycle that complicates the detection process. In this paper, we introduce a Multi-Agent System (MAS) as an adaptive
intelligent technique that acts on top of distributed Case-Based Reasoning (CBR) Phishing Detection Systems (CBR-PDSs) as a
Phishing Detection System Architecture (PDSA) that runs on large scale globally to constitute a robust worldwide Phishing Threat
Intelligence (PTI) environment. The global collaborations of PTI introduces a proactive phishing detection technique, quarantines
phishing threats via global threats sharing, and minimizes users’ susceptibilities to hard-to-detect spear or advanced phishing
attacks. Also, combining two intelligent systems in a unified interactive architecture facilitates the prediction process, increases the
accuracy rate, easily tackles the dynamic and changeable behaviors of advanced phishing threats, and minimizes the false negative
rate as well. The proposed architecture illustrates the consolidated interaction between intelligent agents and distributed CBR-PDSs
in a PTI framework.
Keywords: Phishing Detection | Agents Technology | Case-Based Reasoning | Distributed Systems |
مقاله انگلیسی |
6 |
Persisting big-data: The NoSQL landscape
تداوم داده های بزرگ :چشم انداز NoSQL-2017 The growing popularity of massively accessed Web applications that store and analyze
large amounts of data, being Facebook, Twitter and Google Search some prominent
examples of such applications, have posed new requirements that greatly challenge tra
ditional RDBMS. In response to this reality, a new way of creating and manipulating data
stores, known as NoSQL databases, has arisen. This paper reviews implementations of
NoSQL databases in order to provide an understanding of current tools and their uses.
First, NoSQL databases are compared with traditional RDBMS and important concepts are
explained. Only databases allowing to persist data and distribute them along different
computing nodes are within the scope of this review. Moreover, NoSQL databases are
divided into different types: Key-Value, Wide-Column, Document-oriented and Graph
oriented. In each case, a comparison of available databases is carried out based on their
most important features.
Keywords:NoSQL databases|Relational databases|Distributed systems|Database persistence|Database distribution|Big data |
مقاله انگلیسی |
7 |
Failure Analysis and Prediction for Big-Data Systems
تحلیل شکست و پیش بینی برای سیستم های داده های بزرگ-2016 Motivated by the high complexity of today’s datacenters, a large body of studies tries to understand workloads and resource
utilization in datacenters. However, there is little work on exploring unsuccessful job and task executions. In this article, we study and
predict three types of unsuccessful executions in traces of a Google datacenter, namely fail, kill, and eviction. We first quantitatively
show their strongly negative impact on machine time and the resulting task slowdown. We analyze patterns of unsuccessful jobs and
tasks, particularly focusing on their interdependencies, and we uncover their root causes by inspecting key workload and system
attributes. Furthermore, we develop three on-line prediction models that can classify jobs and events into four classes upon arrival
time, using independent or nested Neural Networks. We explore different combinations of feature sets and techniques to reduce the
computational overhead. Our evaluation results show that the proposed models can accurately classify 94.4% of jobs and 76.8% of
events into four classes.
Index Terms: Distributed Systems | Reliability | availability and serviceability | Neural nets. |
مقاله انگلیسی |
8 |
Privacy in Internet of Things: A Model and Protection Framework
حفظ حریم خصوصی در اینترنت اشیاء: مدل و چارچوب حفاظت-2015 A new form of computation is being evolved to include massive number of diverse set of conventional computing systems, sensors, devices, equipments, software and information services and apps. This new form of computing environment is known as the “Internet-of-Things” (IoT). The adoption of IoT is fast and the “things” are becoming integral part of people day-to-day life as well as essential elements in the businesses everyday activities and processes. Open characteristics of IoT environments raises privacy concern as “things” are autonomous with some degree of authority to sharing their capabilities and knowledge to fulfil their individual or collective tasks. As such privacy becomes central and an inherit computational aspect of the “things”. The work presented here is based on modelling IoT as Cooperative Distributed Systems (CDS). It proposes a novel approach of analysing and modelling privacy concepts and concerns. Privacy protection is captured as a form of “sensitive information” management at the interaction level. A privacy protection management framework for CDS at the interaction level is proposed. The application of the framework has been demonstrated by extending Contract Net Protocol (CNP) to support privacy protection for CDS.
Keywords: Privacy | IoT | Cooperative Distributed Systems (CDS) |
مقاله انگلیسی |
9 |
Trends in big data analytics
روندهایی در تجزیه و تحلیل داده های بزرگ-2014 One of the major applications of future generation parallel and distributed systems is in big-data analytics.
Data repositories for such applications currently exceed exabytes and are rapidly increasing in size.
Beyond their sheer magnitude, these datasets and associated applications’ considerations pose significant
challenges for method and software development. Datasets are often distributed and their size and privacy
considerations warrant distributed techniques. Data often resides on platforms with widely varying
computational and network capabilities. Considerations of fault-tolerance, security, and access control are
critical in many applications (Dean and Ghemawat, 2004; Apache hadoop). Analysis tasks often have hard
deadlines, and data quality is a major concern in yet other applications. For most emerging applications,
data-driven models and methods, capable of operating at scale, are as-yet unknown. Even when known
methods can be scaled, validation of results is a major issue. Characteristics of hardware platforms and the
software stack fundamentally impact data analytics. In this article, we provide an overview of the stateof-the-art and focus on emerging trends to highlight the hardware, software, and application landscape
of big-data analytics.
Keywords:
Big-data
Analytics
Data centers
Distributed systems |
مقاله انگلیسی |
10 |
روند تجزیه و تحلیل داده های بزرگ
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 43 یکی از کاربردهای عمده نسل آتی سیستم های موازی و توزیع شده، مربوط به تحلیل داده های بزرگ است. مخازن داده برای چنین کاربردهایی امروزه بیش از چندین اگزابایت بوده و به سرعت نیز در حال افزایش هستند. علیرغم حجم بسیار زیاد این مخازن، این دیتاست ها و همچنین اپلیکیشن های نظیر آنها، چالش های عمده ای را برای متدها و نرم افزارهای برنامه نویسی مربوطه ایجاد کرده اند. دیتاست ها معمولا توزیع شده بوده و نیز حجم آنها و دسترسی مجاز به آنها توسط تکنیک های توزیع شده تضمین شده است. داده ها معمولا روی یک پلت فورم با قابلیت محاسباتی و شبکه ای بالا، مقیم هستند. توجه به میزان تحمل خطا، امنیت، و کنترل دسترسی موضوع مهمی در بسیاری از کاربردهاست (Dean and Ghemawat, 2004; Apache hadoop). تسک های (task) تحلیلی معمولا ضرب العجل های معینی دارند و نیز در آنها کیفیت داده ها مهم ترین مخاطره نسبت به دیگر کاربردهاست. برای بیشتر کاربردهای درحال ظهور، مدل ها و متدهای مبتنی بر داده، که قادر به عملیات در مقیاس های مختلف هستند، هنوز برایمان ناشناخته است. حتی درصورتی که متدهای شناخته شده مقیاس پذیر باشند، اعتبارسنجی نتایج آنها موضوع مهمی خواهد بود. مشخصات پلت فورم های سخت افزاری و نیز پشته های نرم افزاری، اساسا تحلیل داده ها را تحت تاثیر قرار داده اند. در این مقاله، ما با مرور به روزترین تکنولوژی، بررسی بر گرایش های در حال ظهور در در این زمینه خواهیم داشت تا براین اساس تشریحی بر سخت افزار، نرم افزار و دورنمای کاربردی تحلیل داده های بزرگ ارائه دهیم.
کلمات کلیدی: داده های بزرگ | تجزیه و تحلیل | مراکز داده | سیستم های توزیع شده |
مقاله ترجمه شده |