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Purpose – The purpose of this paper is to integrate and optimize a multiple big data processing platform with the features of high performance, high availability and high scalability in big data environment. Design/methodology/approach – First, the integration of Apache Hive, Cloudera Impala and BDAS Shark make the platform support SQL-like query. Next, users can access a single interface and select the best performance of big data warehouse platform automatically by the proposed optimizer. Finally, the distributed memory storage system Memcached incorporated into the distributed file system, Apache HDFS, is employed for fast caching query results. Therefore, if users query the same SQL command, the same result responds rapidly from the cache system instead of suffering the repeated searches in a big data warehouse and taking a longer time to retrieve. Findings – As a result the proposed approach significantly improves the overall performance and dramatically reduces the search time as querying a database, especially applying for the high-repeatable SQL commands under multi-user mode. Research limitations/implications – Currently, Shark’s latest stable version 0.9.1 does not support the latest versions of Spark and Hive. In addition, this series of software only supports Oracle JDK7. Using Oracle JDK8 or Open JDK will cause serious errors, and some software will be unable to run. Practical implications – The problem with this system is that some blocks are missing when too many blocks are stored in one result (about 100,000 records). Another problem is that the sequential writing into In-memory cache wastes time. Originality/value – When the remaining memory capacity is 2 GB or less on each server, Impala and Shark will have a lot of page swapping, causing extremely low performance. When the data scale is larger, it may cause the JVM I/O exception and make the program crash. However, when the remaining memory capacity is sufficient, Shark is faster than Hive and Impala. Impala’s consumption of memory resources is between those of Shark and Hive. This amount of remaining memory is sufficient for Impala’s maximum performance. In this study, each server allocates 20 GB of memory for cluster computing and sets the amount of remaining memory as Level 1: 3 percent (0.6 GB), Level 2: 15 percent (3 GB) and Level 3: 75 percent (15 GB) as the critical points. The program automatically selects Hive when memory is less than 15 percent, Impala at 15 to 75 percent and Shark at more than 75 percent.

Purpose The purpose of this paper is to propose a new population-based metaheuristic optimization algorithm, assimilation-accommodation mixed continuous ant colony optimization (ACACO), to improve the accuracy of Takagi-Sugeno-Kang-type fuzzy systems design. Design/methodology/approach The original N solution vectors in ACACO are sorted and categorized into three groups according to their ranks. The Research Learning scheme provides the local search capability for the best-ranked group. The Basic Learning scheme uses the ant colony optimization (ACO) technique for the worst-ranked group to approach the best solution. The operations of assimilation, accommodation, and mutation in Mutual Learning scheme are used for the middle-ranked group to exchange and accommodate the partial information between groups and, globally, search information. Only the N top-best-performance solutions are reserved after each iteration of learning. Findings The proposed algorithm outperforms some reported ACO algorithms for the fuzzy system design with the same number of rules. The performance comparison with various previously published neural fuzzy systems also shows its superiority even with a smaller number of fuzzy rules to those neural fuzzy systems. Research limitations/implications Future work will consider the application of the proposed ACACO to the recurrent fuzzy network. Originality/value The originality of this work is to mix the work of the well-known psychologist Jean Piaget and the continuous ACO to propose a new population-based optimization algorithm whose superiority is demonstrated.

In this paper, we present a study of genetic-based stock selection models using the data of fundamentals of initial public offerings (IPOs). The stock selection model intends to derive the relative quality of the IPOs in order to obtain their relative rankings. Top-ranked IPOs can be selected to form a portfolio.In this study, we also employ Genetic Algorithms (GA) for optimization of model parameters and feature selection for input variables to the stock selection model. We will show that our proposed models deliver above-average first-day returns.Based upon the promising results obtained, we expect our GA-based methodology to advance the research in soft computing for computational ("mance and provide an effective solution to stock selection for IPOs in practice.Stock selection has long been recognized as a challenging and important research area in finance. Its main application consists of selecting promising stocks out of a universe of regular stocks or initial public offerings (IPOs). The success of this task is highly contingent on reliable models that utilize relevant information to pick stocks to deliver above-average returns in the future (for regular stocks) or first-day returns (for IPOs).In contrast to traditional approaches, such as regression-based methods, recent advances in computational intelligence (CI) are leading to promising opportunities to solve the problems of stock selection more effectively [1] . In the past, interesting CI methods developed for tackling this task include fuzzy inference models, artificial neural networks (ANNs), support vector machines (SV Ms), as well as evolutionary algorithms (EAs). For instance, in the area of fuzzy model applications, earlier work includes Chu et al. 's 978-1-4673-1487-9/12/$31.00 ©2012 IEEE fuzzy multiple attribute decision analysis to select stocks for portfolio construction [2] . Analogously, Zargham and Sayeh [3] employed a fuzzy rule-based system to evaluate a set of stocks for the same task. These fuzzy approaches denote early efforts in employing CI for the problems of stock selection, but they usually lack sufficient learning ability.Quah and Srinivasan [4] studied an ANN stock selection system to choose stocks that are top-ranked performers. They showed their proposed model outperformed the benchmark model in terms of compounded actual returns. Chapados and Bengio [5] also trained neural networks for estimation and prediction of asset behavior in order to facilitate decision-making in asset allocation. Although these models have been shown to work in some applications, they often suffer from overfitting problems.Caplan and Becker [6] , and Becker, Fei and Lester [7] employed genetic programming (GP) to develop stock ranking models for the U.S. market. Although these methods seemed to work in some applications, it is often difficult for human experts to use the resultant complicated models for straightforward decision making. In contrast to these complicated models, simpler and more intuitive models were developed. For example, Kim and Han ...

Abstract. As the old revolutionary base areas in Yan'an, it is possible to combine the local environmental advantages and hold the special marathon of "re-running the Long March Road", and use it as a city card. The Yan'an city government has supported Yan'an to hold the "Run-of-the-Long March" marathon; Yan'an has sufficient economic strength to hold the "Long-run Long March", which will be carried out by Yan'an Municipal Government. Yan'an spirit has a good foundation and environment for the hosting and future development of the "Running Long March" marathon; Yan'an's natural environment has obvious advantages, the event venue can be integrated into the natural landscape, due to the Beijing Olympic torch The success of the transfer, the technical conditions are very mature, fully capable of hosting the event.The development of marathon in China is influenced by factors such as the level of competition, the level of management, the enthusiasm of the management and the enthusiasm of the registration. Metropolis will still lead the trend of the marathon, but other regions, especially the western region of China, And other advantages of tourism resources to organize a unique event, can also be another bright spot. However, whether the city has the conditions and ability to organize large-scale sports events, you need to host from the environment, culture, economy three aspects of research, this paper through Yan'an's natural environment, Yan'an city culture, Yan'an social economy three aspects Research and analysis of Yanan suitable for organizing the "re-run the Long March" special marathon race reasons.