Energy big data: A survey <s> B. A THREE-TIER ENERGY BIG DATA ANALYTICS ARCHITECTURE <s> Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems. <s> BIB001 </s> Energy big data: A survey <s> B. A THREE-TIER ENERGY BIG DATA ANALYTICS ARCHITECTURE <s> The ? -Support Vector Regression ( ? -SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ? on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to ? -Support Vector Classification ( ? -SVC) (Scholkopf et?al., 2000), ? -SVR introduces an additional linear term into its objective function. Thus, directly applying the accurate on-line ? -SVC algorithm (AONSVM) to ? -SVR will not generate an effective initial solution. It is the main challenge to design an incremental ? -SVR learning algorithm. To overcome this challenge, we propose a special procedure called initial adjustments in this paper. This procedure adjusts the weights of ? -SVC based on the Karush-Kuhn-Tucker (KKT) conditions to prepare an initial solution for the incremental learning. Combining the initial adjustments with the two steps of AONSVM produces an exact and effective incremental ? -SVR learning algorithm (INSVR). Theoretical analysis has proven the existence of the three key inverse matrices, which are the cornerstones of the three steps of INSVR (including the initial adjustments), respectively. The experiments on benchmark datasets demonstrate that INSVR can avoid the infeasible updating paths as far as possible, and successfully converges to the optimal solution. The results also show that INSVR is faster than batch ? -SVR algorithms with both cold and warm starts. <s> BIB002
A widely accepted analytical framework of big data is shown in Fig. 2 , which has three layers including data access and computing, data privacy, domain knowledge and big data mining algorithm BIB001 . The inner core data mining platform is mainly responsible for data access and computation process. With the increasing 3846 VOLUME 4, 2016 FIGURE 2. Three-tier big data analytics architecture. growth of the data volume, distributed storage of large scale data need to be taken into account while computing. That is to say, data analysis and task processing are divided into multiple sub tasks and executed on a large number of computing nodes through a parallel program. The middle layer of the structure plays an important role to connect inner layer and outer one. The data mining technology in the inner layer provides a platform for data-related work in middle layer such as the information sharing, privacy protection, and knowledge acquisition from areas and applications, etc. In the whole process, the information sharing is not only the guarantee of each phase, but also the goal of processing and analyzing with the big data in smart grid. In the outer layer of the architecture, preprocessing is necessary for the heterogeneous, uncertain, incomplete, and multi-source data through data fusion technology. After preprocessing, complex and dynamic data will be excavated, and then, pervasive smart grid global knowledge can be obtained through local learning and model fusion. Finally, model and its parameters need adjustment according to the feedback BIB002 .