The DaaS (Data as a Service) is an Internet-perception big data system based on AI, which is built by "Think Tank 2861 Project Team". This is an Internet-area, data-based, and neural feedback system for the Internet information in China. It takes Internet activities as the input, and processes through AI algorithms and machine learning framework to generate the output, based on which building the real-time macro economics and society big data for about 9.8 million grids in China and its intelligent applications. DaaS covers all 2,861 administrative districts and counties in the country and is refined to geography grid of one square kilometer granularity. The real-time objective information generated by distributed AI algorithms, that are constantly trained and calibrated, is of great value in scientific research and commercial applications.

Influence maximization is a very hot research in social network. However, it is difficult to find a good algorithm to keep balance between the time complexity and computing result' accuracy. In order to solve this problem, in this paper, we propose two new algorithms. Firstly, we present a heuristic algorithm based on the greedy algorithm, which can reduce the time complexity a lot and it will have a good result, too. Then, we present another new algorithm. We use the k-means idea to solve the IM problem. We use the k-means idea to find s seed nodes. At the same time, we prove these two new algorithms.

This article is composed in the background of the study of scientific field of coauthors phenomenon factual basis. By the study of massive amounts of relational data, it provides us with major significances theoretically and practically on retrieving and obtaining professionally academic information and getting knowing of academic development trend of miscellaneous fields. In process of studying this type of project, the problem of cluttering for coauthors that are in the data is involved. However, it is hard to meet the need of implementing the analysis of massive amounts of data cluttering by the existing cluttering software and algorithms, for this reason, finding an approach to deal with this kind of question is toughly important. To solve this question, this article presents an optimized Bisecting K-Means (BKM) clustering algorithm based on Hadoop and states the fashion of how to optimize the algorithm and the key point of implementing in details after analyzing the status quo related to this study. Estimating the complexity of the algorithm by experiments indicates the current problems and the direction for the future study.

With the development of cloud computing technologies, big data processing is becoming more and more important. How to mine and analyze massive data is facing a very big challenge. In this paper, we proposed an efficient massive data mining and analysis framework Data Cloud on large clusters. The most important part of Data Cloud is the Rabbit. It is a kind of massive data mining and analysis processing plan framework on the large clusters like the Pig and Hive. We make a detail analysis about the Rabbit plan.

Massive rules processing has attracted more attention in recently years. Firstly, we propose a rule description language that can express all kind of rules by structured nature language. We design a set of graphical symbols for rule nodes. We also propose a rule traffic flow model and a rule cost model. Thought these models, it is easier to process massive numbers rules and optimize them.