The communication model of Internet of Things (IOT) application has some shortcomings in user privacy protection and information security. To solve these shortcomings, we define the formal models of certificateless online/offline signcryption and propose a concrete certificateless online/offline signcryption scheme for IOT environment. Compared with the existing identity‐based online/offline signcryption schemes that do not require the plaintext and the receiver's identity in the offline phase, our scheme has the great advantage of the offline computation cost, offline storage, ciphertext length, and receiver computation cost. Moreover, our scheme achieves known session‐specific temporary information security, public verifiability with confidentiality and no key escrow problem. Copyright © 2013 John Wiley & Sons, Ltd.

This article describes how the public traffic system evaluation is an important measure to strengthen the management of urban transportation. Many scholars have evaluated the public transportation system, but lack research on different index weights of it. In past models, although the fuzzy assessment method was integrated into an evaluation methodology, its randomness was reflected unclearly. To solve the problems, a fuzzy evaluation of a cloud model is researched. Firstly, the corresponding weights of all indexes are calculated by analytic hierarchy process (AHP) and a clustering method. Then, the principal component of the indexes is extracted by the principal component analysis. According to the distribution of a principal component and processed with the cloud model, a subordinate degree function was established. Finally, scoring cities by combining the principal component weight and membership cloud matrix and evaluating the public transportation system. Comparing the matter-element analysis and the AHP gray model method, this proposed model in this article can evaluate the performance of different urban traffic systems more practically.

No abstract

Data ingestion is an essential part of companies and organizations that collect and analyze large volumes of data. This paper describes Gobblin, a generic data ingestion framework for Hadoop and one of LinkedIn's latest open source products. At LinkedIn we need to ingest data from various sources such as relational stores, NoSQL stores, streaming systems, REST endpoints, filesystems, etc. into our Hadoop clusters. Maintaining independent pipelines for each source can lead to various operational problems. Gobblin aims to solve this issue by providing a centralized data ingestion framework that makes it easy to support ingesting data from a variety of sources. Gobblin distinguishes itself from similar frameworks by focusing on three core principles: generality, extensibility, and operability. Gobblin supports a mixture of data sources out-of-the-box and can be easily extended for more. This enables an organization to use a single framework to handle different data ingestion needs, making it easy and inexpensive to operate. Moreover, with an end-to-end metrics collection and reporting module, Gobblin makes it simple and efficient to identify issues in production.

This paper focuses on how to better build a model from resource aggregation to analytics mining in mainstream cloud computing and big data. Distributed Big Data Analysis Modeling Research, provide high reliability, high security and high efficiency services in analysis and judgement for various intelligence analysis and intelligent decision-making.