تقویت قابلیت های سیستم های پشتیبان تصمیم گیری خدمات گرا: انتقال تحلیل ها و داده های بزرگ به محیط ابری

Using service-oriented decision support systems (DSS in cloud) is one of the major trends for many organizations in hopes of becoming more agile. In this paper, after defining a list of requirements for service-oriented DSS, we propose a conceptual framework for DSS in cloud, and discus about research directions. A unique contribution of this paper is its perspective on how to servitize the product oriented DSS environment, and demonstrate the opportunities and challenges of engineering service oriented DSS in cloud. When we define data, information and analytics as services, we see that traditional measurement mechanisms, which are mainly time and cost driven, do not work well. Organizations need to consider value of service level and quality in addition to the cost and duration of delivered services. DSS in CLOUD enables scale, scope and speed economies. This article contributes new knowledge in service science by tying the information technology strategy perspectives to the database and design science perspectives for a broader audience.

In today's very complex business world, organizations must find innovative ways to differentiate themselves from competitors by becoming more collaborative, virtual, accurate, synchronous, adaptive and agile. They need to be able to rapidly respond to market needs and changes. Many organizations noticed that the data they own and how they use it can make them different than others. Data and information are becoming primary assets for many organizations. That's why, today, most organizations try to collect and process as much data as possible. According to the Gartner Research, the worldwide market for data warehousing and business intelligence solutions is forecasted to reach US$10.8 billion in 2011 [34]. And it is ranked number five on the list of the top ten technology priorities for chief information officers in 2011. That's why having efficient and effective decision making processes with right data that is transformed to be meaningful information with data-driven discoveries (e.g. analytics) are becoming mainstream processes for companies to run smarter, more agile and efficient businesses [13]. There also are data related challenges for organizations. For instance, there is the challenge of managing large amounts of data (big data), which is getting increasingly larger because of cheaper storage and evolution of digital data and information collection devices, such as cell phones, laptops, and sensors. For example, Facebook, a social-networking website, is a home to 40 billion photos, and Wal-Mart handles more than 1 million customer transactions every hour, feeding databases estimated at more than 2.5 petabytes. There are 4.6 billion mobile-phone subscriptions worldwide and 1–2 billion people use the internet [46]. There is no question that we are living in an era of data and information explosion. Also, there are more people who interact with information, and more information is shared. According to the Economist Report [46] between 1990 and 2005 more than 1 billion people worldwide entered the middle class, and by 2013 the amount of data transferred over the internet will reach 667 exabytes annually, and according to Cisco the quantity of data continues to grow faster than the ability of the network to carry it. Companies like Amazon's Web Services, AT&T's Synaptic Hosting, AppNexus, GoGrid, Rackspace Cloud Hosting, the HP/Yahoo/Intel Cloud Computing Testbed, the IBM/Google and MicroStrategy BI Cloud are providing various types of clouds services to ease the data storage problems. Besides the challenges posed by fast growing amount of data, there are also ample opportunities for the world as it becomes more and more digital allowing context-specific aggregation and analysis of data. For example, information and/or knowledge extracted from digital records can make doctors' job easier in accurately diagnosing and treating illnesses, and bring down healthcare costs for providers and patients, and hence improve the overall quality and efficiency of healthcare [15]. Similarly, digitized data (institutional and public—mostly internet-based) can be accessed and analyzed to bolster success on fighting crime more effectively and efficiently. Service-oriented thinking is one of the fastest growing paradigms in information technology, with relevance to many other disciplines such as accounting, finance and operations [16]. According to Babaie et al. [4], worldwide end-user spending on IT services will grow at a 6.4% compound annual growth rate through 2010 to reach US$855.6 billion, with positive growth in nearly all market segments. As a future trend, Gartner predicts that at least one-third of business application software spending will be on software-as-a-service, instead of as product licenses, by 2012. Also, 40% of capital expenditures will be made for infrastructure as a service by 2011 [38]. And more recently, Pike Research expects the growth in cloud computing revenue to continue worldwide between now and 2015 at a compound annual growth rate of 28.8%, with the market increasing from $46.0 billion in 2009 to $210.3 billion by 2015 [37]. For many companies (especially small and medium size), the pay-as-you-go service-oriented computing model (cloud computing), with having someone else worrying about maintaining the hardware and software are becoming very attractive [31]. Cloud computing is reminiscent of the software-as-a-service, infrastructure-as-a-service, database-as-a-service paradigms [6]. Cloud computing platforms, like those offered by Amazon Web Services, AT&T's Synaptic Hosting, AppNexus, GoGrid, Rackspace Cloud Hosting, and to an extent, the HP/Yahoo/Intel Cloud Computing Testbed, and the IBM/Google cloud maintain more than the hardware, and give customers a set of virtual machines in which to install their own software. Resource availability is typically elastic, with a seemingly infinite amount of computing power and storage space available on demand, in a pay-only-for-what-you-use pricing model [1]. In this paper, we adopt and use the National Institute of Standards and Technology's (NIST) definition for “cloud.” NIST defines cloud computing as “… a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (for example, networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” [33]. Given the emerging services paradigm, it is time to rethink IT and IS from new organizational and technical vantage points [39]. After briefly reviewing the impact of service-orientation on information systems, herein we present a conceptual model that supports evaluation of theory and methods for the management of service-oriented decision support systems (SODSS). Then, based on the proposed conceptual model, we explore some of the pressing issues and promising opportunities and their potential contributions to the new managerial knowledge of SODSS. In the next section, we begin with a brief overview of the foundation for “services paradigm” and identify the key requirements for SODSS. In 3 and 4, we conceptualize the SODSS, and review the relevant and leading methods, models and theories, and then discuss about where foundational knowledge of the emerging service-orientation (in cloud type environment) can be developed. The last section summarizes our contributions and lists the limitations of this research.

In this section, we first summarize the main contributions of our work and then discuss the limitation and future research directions. 5.1. Contributions Most organizations today are fundamentally dependent on their data and information handling services facilitated by their information technology [36] to collect, store, flow, manage and analyze data better. This paper provided a list of requirements for DSS in order to address today and tomorrow's needs. And then it proposed a conceptual framework for service-oriented DSS, evaluated the existing literature, the current applications and solutions, and proposed research directions. A unique contribution of this paper is its perspective on how to servitize the product oriented DSS environment, and demonstrate the opportunities and challenges of engineering DSS in cloud environment. When we define data, information and analytics we see that the traditional measurement mechanisms do not work efficiently. Organizations may care about service accuracy and quality in addition to the cost and delivery time. Service-oriented DSS (DSS in cloud) proposes scale, scope and speed economies. Basically, reduction in unit service costs due to increase in operational size (scale), reduction in unit service costs due to increase in number of services being developed and provided (scope) and reduction in unit costs due to increase in number of services put through supply/demand chain (speed). 5.2. Limitations and future work There are additional and important theories and models that we have not fully addressed. For example, we did not discuss in detail how service orientation will impact the operations of DSS environment. How should we educate new DBAs, data engineers, data analysts and users for DSS in cloud? Second, we have not analyzed the service provider's site of the research issues. Service providers need new approaches to be able to manage their capacity and pricing decisions efficiently. What will be the dynamics in service and price competition? Third, it also will be beneficial if future research examines the challenges and opportunities for governments and international organizations. What will be the tax policies and procedures, when service providers are hosting their virtual data bases in different countries and providing services to different countries? In this article, we had no intention to present an exhaustive survey of research articles, nor did we intend to offer a comprehensive reading on the research agenda for service-oriented DSS. We simply wanted to propose a new conceptual architecture for DSS in cloud, and identify research questions to fully realize this promising endeavor.