Harnessing the strengths of anytime algorithms for constant data streams

Anytime algorithms have been proposed for many different applications, e.g., in data mining. Their strengths are the ability to first provide a result after a very short initialization and second to improve their result with additional time. Therefore, anytime algorithms have so far been used when the available processing time varies, e.g., on varying data streams. In this paper we propose to employ anytime algorithms on constant data streams, i.e., for tasks with constant time allowance. We introduce two approaches that harness the strengths of anytime algorithms on constant data streams and thereby improve the over all quality of the result with respect to the corresponding budget algorithm. We derive formulas for the expected performance gain and demonstrate the effectiveness of our novel approaches using existing anytime algorithms on benchmark data sets.     

视频分析技术的研究和应用

通过对视频分析关键技术的探讨及国内外研究小组科研成果的分析.揭示了视频编码技术、视频对象分割技术、基于内容检索技术、流媒体传输技术之间的内在联系以及各自的技术要点、难点.通过介绍视频分析技术在视频监控中的应用,展望了该技术在多个领域的广阔的应用前景.     

Distinguishing attacks on stream ciphers based on arrays of pseudo-random words

In numerous modern stream ciphers, the internal state consists of a large array of pseudo-random words, while the output key-stream is a relatively simple function of the state. It has been heuristically shown in several situations [3], [8], [9], [10], [11] and [14] that this structure may lead to distinguishing attacks on the cipher. In this note we present a more rigorous treatment of this structural attack. First, we present a rigorous proof of the main probabilistic claim behind it in the basic cases. We then apply it concretely to the cipher sn3 [12], and demonstrate that the heuristic assumptions of the attack are remarkably precise in more complicated cases.     

Worst-case optimal algorithm for XPath evaluation over XML streams

We consider the XPath evaluation problem: Evaluate an XPath query Q on a streaming XML document D; i.e., determine the set Q(D) of document elements selected by Q. We mainly consider Conjunctive XPath queries that involve only the child and descendant axes. Previously known in-memory algorithms for this problem use O(|D|) space and O(|Q||D|) time. Several previously known algorithms for the streaming version use Ω(dn) space and Ω(dn|D|) time in the worst case; d denotes the depth of D, and n denotes the number of location steps in Q. Their exponential space requirement could well exceed the O(|D|) space used by the in-memory algorithms. We present an efficient algorithm that uses O(d|Q|+nc) space and O((|Q|+dn)|D|) time in the worst case; c denotes the maximum number of elements of D that can be candidates for output, at any one instant. For some worst case Q and D, the memory space used by our algorithm matches our lower bound proved in a different paper; so, our algorithm uses optimal memory space in the worst case.     

区分服务网络中SCTP协议的改进

针对双窗口算法难以判断包丢弃类型的问题,提出一种新的包丢弃类型判断方法,对SCTP协议的确认块进行扩展,从确认块中获取包丢弃类型信息。在NS2上进行仿真实验,实验结果表明,改进后的SCTP协议在吞吐率方面有显著提高。     

Real-time data analytics and event detection for IoT-enabled communication systems

Enterprise Communication Systems are designed in such a way to maximise the efficiency of communication and collaboration within the enterprise. With users becoming mobile, the Internet of Things (IoT) can play a crucial role in this process, but is far from being seamlessly integrated into modern online communications. In this paper, we present a semantic infrastructure for gathering, integrating and reasoning upon heterogeneous, distributed and continuously changing data streams by means of semantic technologies and rule-based inference. Our solution exploits semantics to go beyond today’s ad-hoc integration and processing of heterogeneous data sources for static and streaming data. It provides flexible and efficient processing techniques that can transform low-level data into high-level abstractions and actionable knowledge, bridging the gap between IoT and online Enterprise Communication Systems. We document the technologies used for acquisition and semantic enrichment of sensor data, continuous semantic query processing for integration and filtering, as well as stream reasoning for decision support. Our main contributions are the following, (i) we define and deploy a semantic processing pipeline for IoT-enabled Communication Systems, which builds upon existing systems for semantic data acquisition, continuous query processing and stream reasoning, detailing the implementation of each component of our framework; (ii) we present a rich semantic information model for representing and linking IoT data, social data and personal data in the Enterprise Communication scenario, by reusing and extending existing standard semantic models; (iii) we define and develop an expressive stream reasoning component as part of our framework, based on continuous query processing and non-monotonic reasoning for semantic streams, (iv) we conduct experiments to comparatively evaluate the performance of our data acquisition and semantic annotation layer based on OpenIoT, and the performance of our expressive reasoning layer in the scenario of Enterprise Communication.     

A syntactic approach to twig-query matching on XML streams

Query matching on XML streams is challenging work for querying efficiency when the amount of queried stream data is huge and the data can be streamed in continuously. In this paper, the method Syntactic Twig-Query Matching (STQM) is proposed to process queries on an XML stream and return the query results continuously and immediately. STQM matches twig queries on the XML stream in a syntactic manner by using a lexical analyzer and a parser, both of which are built from our lexical-rules and grammar-rules generators according to the user's queries and document schema, respectively. For query matching, the lexical analyzer scans the incoming XML stream and the parser recognizes XML structures for retrieving every twig-query result from the XML stream. Moreover, STQM obtains query results without a post-phase for excluding false positives, which are common in many streaming query methods. Through the experimental results, we found that STQM matches the twig query efficiently and also has good scalability both in the queried data size and the branch degree of the twig query. The proposed method takes less execution time than that of a sequence-based approach, which is widely accepted as a proper solution to the XML stream query.     

数据库服务器与WWW之间的一种集成设计

本文根据WWW用户和数据库用户各自对数据访问的特点不同,提出了一种将数据库服务器与WWW集成的结构。该集成结构中既提供了基于超文本的导航浏览功能访问数据库系统,又支持用户直接利用数据库系统本身的查询界面,快速查询数据对象。     

混合监测技术在并行与分布式性能监测中的应用

主要介绍了混合监测技术在并行与分布式系统性能分析评价中的应用。分析比较了常用的几种监测技术,给出了各自的优缺点,介绍了混合监测中的事件的定义,自动装备的过程。事件的采集和分析这向个关键步骤,提出装备的层交化。最后提出了混合监测中会遇到一些问题并给出了相应的解决方法。