Abstract:Knapsack problem (KP) is a well-known combinatorial optimization problem which includes 0-1 KP, bounded KP, multi-constraint KP, multiple KP, multiple-choice KP, quadratic KP, dynamic knapsack KP, discounted KP and other types of KPs. KP can be considered as a mathematical model extracted from variety of real fields and therefore has wide applications. Evolutionary algorithms (EAs) are universally considered as an efficient tool to solve KP approximately and quickly. This paper presents a survey on solving KP by EAs over the past ten years. It not only discusses various KP encoding mechanism and the individual infeasible solution processing but also provides useful guidelines for designing new EAs to solve KPs.

Abstract:The vigorous development of positioning technology and pervasive computing has given rise to trajectory big data, i.e. the high speed trajectory data stream that originated from positioning devices. Analyzing trajectory big data timely and effectively enables us to discover the abnormal patterns that hide in trajectory data streams, and therefore to provide effective support to applications such as urban planning, traffic management, and security controlling. The traditional anomaly detection algorithms cannot be applied to outlier detection in trajectory big data directly due to the characteristics of trajectories such as uncertainty, un-limitedness, time-varying evolvability, sparsity and skewness distribution. In addition, most of trajectory outlier detection methods designed for static trajectory dataset usually assume a priori known data distribution while disregarding the temporal property of trajectory data, and thus are unsuitable for identifying the evolutionary trajectory outlier. When dealing with huge amount of low-quality trajectory big data, a series of issues need to be addressed. Those issues include coping with the concept drifts of time-varying data distribution in limited system resources, online detecting trajectory outliers, analyzing causal interactions among traffic outliers, identifying the evolutionary related trajectory outlier in larger spatial-temporal regions, and analyzing the hidden abnormal events and the root cause in trajectory anomalies by using application related multi-source heterogeneous data. Aiming at solving the problems mentioned above, this paper reviews the existing trajectory outlier detecting techniques from several categories, describes the system architecture of outlier detection in trajectory big data, and discusses the research directions such as outlier detection in trajectory stream, visualization and evolutionary analysis in trajectory outlier detection, benchmark for trajectory outlier detection system, and data fusion in semantic analysis for anomaly detection results.

Abstract:In recent years, crowdsourcing, which utilizes the intelligence of crowds to solve problems, provides a novel data processing paradigm for traditional data management challenges and has become one of the hottest research topics. In particular, due to the rapid development of mobile Internet and sharing economy, crowdsourcing not only becomes a new approach for data collection, but is also integrated into all kinds of application scenarios especially spatiotemporal data management such as online-to-offline (O2O) applications, real-time traffic monitoring, and logistics management. In this paper, a survey is provided on existing research of spatiotemporal crowdsourcing. First of all, the concept and representative applications of spatiotemporal crowdsourcing is described, and its relationship with traditional crowdsourcing is explained. Then, the workflow of spatiotemporal crowdsourcing is illustrated. Furthermore, three core research problems and three categories of techniques of spatiotemporal crowdsourcing are discussed. Finally, the state-of-the-art studies of spatiotemporal crowdsourcing are summarized and promising future research directions for the research community are presented.

Abstract:The design of the upper lever database has experienced several rounds of development and transformation to adapt to the changing architecture of the underlying storage system. In the big data era, the emergence of the novel non-volatile memory (NVM) technologies, which exhibit a series of non-volatile (persistent writes), high-capacity, low-latency and byte-addressable characteristics, has brought significant impact on traditional database systems, especially for techniques related to storage and transaction processing. First, in this paper, the phylogeny and development trend of the OLTP database along with the storage subsystem is introduced. Then, the non-volatile memory technology which affects the upper data management system design is reviewed along with an analysis on the domain-oriented and the NVM-oriented transaction technologies. Finally, challenges and opportunities are addressed for the NVM-oriented OLTP database.

Abstract:Research on the influence of social network nodes is one of the key issues in social network analysis. Over the past decade, with the rapid development of online social networks, researchers have the opportunity to analyze and model node influence on many real social networks to achieve fruitful research results which can be applied in a wide range of applications. This paper analyzes and summarizes the main research efforts of social network influence analysis in recent years. First, different definitions of influence, influence functional scope and forms of influence are introduced. Next, models and methods of measuring of node influence are discussed and analyzed in detail with respect to network topology, user behavior and content analysis. Then, literatures about influence spreading and influence maximization model are summarized. Moreover, different indexes for evaluating influence methods are compared, and applications related influence are also presented. Finally, some future research directions on influence analysis are suggested based on the review and analysis of existing research efforts.

Abstract:Network performance measurement, which takes advantage of specific methods and techniques to quantify network performance, is the core branch of the network measurement research. Network performance measurement and analysis provides the most effective and fundamental way of quantifying network performance and characterizing network behavior. Network performance measurement has great significance in network modeling, network security, network management and network optimization. Drawing on the latest research progress, this paper presents the principles, characteristics and implementation mechanisms of the representative algorithms for bandwidth measurement, packet loss measurement and delay measurement. The main discussions include temporal uncertainty and spatial uncertainty during bandwidth measurement, the distinctness of application packet loss and probe packet loss, and the relationship between the clock offset and clock skew. Lastly, the paper discusses some challenges and directions in the field network performance measurement study.

Abstract:Information flow control has been a hot and difficult research topic in providing end-to-end data security. This article presents an overview of the field of information flow control. First, the basic theory and models for information flow control are introduced from the perspectives of lattice, security type system, security process algebra and automata machine. Next, working from the bottom up of the computer hierarchy, the implementation methods of information flow control on hardware, operating system, virtual machine, high-level language, low-level language, database and network are introduced, and a comparison among various studies is provided. Then, combining the new technologies of the current era, the applications of information flow control in cloud computing, mobile internet, IoT (internet of thing) and big data are analyzed. Finally, the current problems and the future trends of information flow control are discussed.

Abstract:Image segmentation is the process of dividing the image into a number of regions with similar properties, and it's the preprocessing step for many image processing tasks. In recent years, domestic and foreign scholars mainly focus on the content-based image segmentation algorithms. Based on extensive research on the existing literatures and the latest achievements, this paper categorizes image segmentation algorithms into three types:graph theory based method, pixel clustering based method and semantic segmentation method. The basic ideas, advantage and disadvantage of typical algorithms belong to each category, especially the most recent image semantic segmentation algorithms based on deep neural network are analyzed, compared and summarized. Furthermore, the paper introduces the datasets which are commonly used as benchmark in image segmentation and evaluation criteria for algorithms, and compares several image segmentation algorithms with experiments as well. Finally, some potential future research work is discussed.