Big Data Reference Architectures, a systematic literature review

Today, we live in a world that produces data at an unprecedented rate. The significant amount of data has raised lots of attention and many strive to harness the power of this new material. In the same direction, academics and practitioners have considered means through which they can incorporate datadriven functions and explore patterns that were otherwise unknown. This has led to a concept called Big Data. Big Data is a field that deals with data sets that are too large and complex for traditional approaches to handle. Technical matters are fundamentally critical, but what is even more necessary, is an architecture that supports the orchestration of Big Data systems; an image of the system providing with clear understanding of different elements and their interdependencies. Reference architectures aid in defining the body of system and its key components, relationships, behaviors, patterns and limitations. This study provides an in-depth review of Big Data Reference Architectures by applying a systematic literature review. The study demonstrates a synthesis of high-quality research to offer indications of new trends. The study contributes to the body of knowledge on the principles of Reference Architectures, the current state of Big Data Reference Architectures, and their limitations

Prevalence of hepatitis A among newly admitted medical students of Isfahan city in 2012

زمینه و هدف: دانشجویان علوم پزشکی معمولاً در معرض تماس با عوامل عفونی مثل هپاتیت A می باشد. این مطالعه سرولوژیکی به منظور بررسی فراوانی نسبی هپاتیت A در بین دانشجویان پزشکی در بدو ورود به دانشگاه علوم پزشکی اصفهان در سال 1390 انجام گرفته است. روش بررسی: در این مطالعه مقطعی (توصیفی- تحلیلی) 403 دانشجوی سال اول به روش نمونه گیری غیر احتمالی آسان انتخاب و نمونه سرم آن ها به منظور تعیین آنتی بادی توتال IgG و IgM علیه هپاتیت A با روش الایزا مورد بررسی قرار گرفت. یافته ها: دانشجویان شامل 252 نفر (5/62 درصد) مرد و 151 نفر (5/37 درصد) زن بودند. شیوع آنتی بادی علیه هپاتیت A، 5/67 درصد ارزیابی شد. در این میان ابتلا به هپاتیت A با محل سکونت (01/0P=) و نوع آب آشامیدنی (018/0P=) ارتباط معنی داری نشان داد. نتیجه گیری: با توجه به در معرض خطر بودن دانشجویان پزشکی، غربالگری اولیه و واکسیناسیون افرادی که علیه هپاتیت A مصونیت ندارند، توصیه می شود

Towards a domain-driven distributed reference architecture for big data systems

The proliferation of digital devices, rapid development of software and the infrastructure of today, have augmented user’s capability to produce data at an unprecedented rate. The accelerated growth of data could be called the era of big data and forced a paradigm shift in data engineering because the variety, velocity and volume of data overwhelmed existing systems. While companies attempt to extract benefit from big data, success rates are still low. Challenges such as rapid changes in technology, organizational culture, complexity in data engineering, impediments to system development, and a lack of effective big data architectures mean that only an estimated 20% of companies achieved their goals. To this end, this study explores a domain-driven distributed big data reference architecture that addresses issues in data architecture, data engineering, and system development. This reference architecture is empirically grounded and evaluated through deployment in a real-world scenario as an instantiated prototype, solving a problem in practice. The results of the evaluation demonstrate utility and applicability but with architectural trade-offs and challenges

Application of microservices patterns to big data systems

Abstract The panorama of data is ever evolving, and big data has emerged to become one of the most hyped terms in the industry. Today, users are the perpetual producers of data that if gleaned and crunched, have the potential to reveal game-changing patterns. This has introduced an important shift regarding the role of data in organizations and many strive to harness to power of this new material. Howbeit, institutionalizing data is not an easy task and requires the absorption of a great deal of complexity. According to the literature, it is estimated that only 13% of organizations succeeded in delivering on their data strategy. Among the root challenges, big data system development and data architecture are prominent. To this end, this study aims to facilitate data architecture and big data system development by applying well-established patterns of microservices architecture to big data systems. This objective is achieved by two systematic literature reviews, and infusion of results through thematic synthesis. The result of this work is a series of theories that explicates how microservices patterns could be useful for big data systems. These theories are then validated through expert opinion gathering with 7 experts from the industry. The findings emerged from this study indicates that big data architectures can benefit from many principles and patterns of microservices architecture