Pattern-based event detection in sensor networks

Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.     

无线传感器网络Top-k多查询算法

针对无线传感器网络中多个Top-k查询问题,提出了一种Top-k多查询处理的算法,对接收到的多个Top-k查询请求进行预处理,预处理依据是约束条件,得出两类不同的查询集合:单约束条件的多查询和多约束条件的多查询。针对单约束条件的多查询提出了ETOP算法,该算法首先对排在时间序列最前面的Top-k查询请求进行基于网内处理,然后把查询结果存入基站缓存,并把结果的最小值设定为阈值传输到各个节点,再根据后续查询请求的查询范围进行相应的查询,从而快速地获得Top-k查询结果。实验表明:Top-k多查询方法在能够很好地实现查询的同时,减少了无线传感器网络中的传输消耗和能量消耗。     

基于活动的社交网络中的群组推荐算法设计

在基于活动的社交网络（EBSN）中,群组中聚集了具有相似兴趣的用户,并为用户组织并举办线下活动,在社区的发展中起到了至关重要的作用,因而理解用户加入群组的原因和群组形成的过程在社交网络的研究中是一个重要的议题.本文通过基于活动的社交网络中的一些相关内容信息,比如社交网络中的标签信息和地理位置信息,来辅助推荐系统更好地为用户预测对于群组的偏好.本文提出了SEGELER （pair-wiSE Geo-social Event-based LatEnt factoR）模型,并使用这些社交网络中的信息,来为用户的兴趣进行预测.通过在真实的EBSN数据集上进行实验与验证,本文的模型不仅可以有效提升对于用户偏好的预测,也可以缓解冷启动问题.     

Sharing RFID and complex event data among organizations

Integrating RFID data from various sources in a value chain is an important requirement for leveraging the full potential of the technology. To facilitate this crucial integration, the global standardization organization GS1 is currently developing a range of service specifications. Yet, the services specified so far do not enable event-based interaction schemes and do not leverage the detection of distributed complex events. In this paper we propose to fill this gap by means of a peer-to-peer based broker network that can realize event-based interaction on top of existing specifications. For this infrastructure we present a new method to improve in-network processing. As shown by our experiments, our method significantly reduces overall system load by exploiting a priori knowledge about the observed processes.     

Dermi: a new distributed hash table-based middleware framework

We disscus about Dermi: a new distributed hash table-based middleware framework. Decentralized event remote method invocation (Dermi) is a peer-to-peer (P2P), decentralized event-based object middleware framework built on top of a structured overlay network. Using an event-notification service as the principal building block, Dermi makes three innovative contributions: P2P call abstractions, distributed interception, and a decentralized object-location service. We describe how to use these three pillars to build a wide variety of global-scale distributed applications and argue that Dermi is a solid foundational technology for future wide-area distributed component infrastructures.     

Efficient processing of aggregation queries in sensor networks

Continuous aggregation queries with a tolerable error threshold have many applications in sensor networks. Since the communication cost is important in the lifetime of sensor networks, there have been a few methods to reduce the communication cost for continuous aggregation queries having a tolerable error threshold. In previous methods, the error threshold in each node is periodically adjusted based on the global statistics collected in the central site that are obtained from all the nodes in the network. These methods require that users specify a few parameters, e.g., adjustment period. However, determination of these parameters by users, in practice, is very difficult and undesirable for sensor network applications demanding unattended operations in dynamically changing environments. In this paper, we propose a new in-network data aggregation protocol, called the Distributed Adaptive Filtering (DAF) protocol. It works in a distributed manner and proceeds adaptively in the sense that the filtering condition in each node is adaptively changed by using only local information. It does not require user parameters that are used in the previous method. We show through various experiments that the proposed method outperforms other existing methods. Recommended by: Ahmed Elmagarmid     

Structuring low-quality videotaped lectures for cross-reference browsing by video text analysis

This paper presents an unified approach in analyzing and structuring the content of videotaped lectures for distance learning applications. By structuring lecture videos, we can support topic indexing and semantic querying of multimedia documents captured in the traditional classrooms. Our goal in this paper is to automatically construct the cross references of lecture videos and textual documents so as to facilitate the synchronized browsing and presentation of multimedia information. The major issues involved in our approach are topical event detection, video text analysis and the matching of slide shots and external documents. In topical event detection, a novel transition detector is proposed to rapidly locate the slide shot boundaries by computing the changes of text and background regions in videos. For each detected topical event, multiple keyframes are extracted for video text detection, super-resolution reconstruction, binarization and recognition. A new approach for the reconstruction of high-resolution textboxes based on linear interpolation and multi-frame integration is also proposed for the effective binarization and recognition. The recognized characters are utilized to match the video slide shots and external documents based on our proposed title and content similarity measures.     

A complex event routing infrastructure for distributed systems

With the growing number of mega services and cloud computing platforms, industrial organizations are utilizing distributed data centers at increasing rates. Rather than the request/reply model, these centers use an event-based communication model. Traditionally, the event-based middleware and the Complex Event Processing (CEP) engine are viewed as two distinct components within a distributed system’s architecture. This division adds additional system complexity and reduces the ability for consuming applications to fully utilize the CEP toolset. This article will address these issues by proposing a novel event-based middleware solution. We introduce Complex Event Routing Infrastructure (CERI), a single event-based infrastructure that serves as an event bus and provides first class integration of CEP. An unstructured peer-to-peer network is exploited to allow for efficient event transmission. To reduce network flooding, superpeers and overlay network partitioning are introduced. Additionally, CERI provides each client node the capability of local complex query evaluation. As a result, applications can offload internal logic to the query evaluation engine in an efficient manner. Finally, as more client nodes and event types are added to the system, the CERI can scale up. Because of these favorable scaling properties, CERI serves as a foundational step in bringing event-based middleware and CEP closer together into a single unified infrastructure component.     

Towards Environment-to-Environment (E2E) multimedia communication systems

We present an approach to connect multiple remote environments over web for natural interaction among people and objects. Focus of current communication and telepresence systems severely restrict user affordances in terms of movement, interaction, peripheral vision, spatio-semantic integrity and even information flow. These systems allow information transfer rather than experiential interaction. We propose Environment-to-Environment (E2E) as a new paradigm for communication which allows users to interact in natural manner using text, audio, and video by connecting environments. Each Environment is instrumented using as many different types of sensors as may be required to detect presence and activity of objects. This object position and activity information is used by a scalable event-based multimodal information system called EventServer to share the appropriate experiential information with other environments as well as to present incoming multimedia information on right displays and speakers. This paper describes the design principles for E2E communication, discusses system architecture, and gives our experience in implementing prototypes of such systems in telemedicine and office collaboration applications. We also discuss the research challenges and a road-map for creating more sophisticated E2E applications in near future.

基于地域特征和异构社交关系的事件推荐算法研究

近几年,在基于事件的社交网络(EBSNs)服务中,为便于增强用户体验,事件推荐任务一直被广泛研究。本文基于对EBSN中用户行为数据的详细分析,提出了一种新型的融合多种数据特征的潜在因子模型。该模型综合考虑EBSN中两种新型的数据特征: 异构的社交关系特征(线上社交关系+线下社交关系)和用户参与行为的地域性特征。基于真实的Meetup数据集,实验结果表明我们的算法在解决事件推荐问题时比传统的算法有更好的性能。
     

一种基于协同上下文关系学习的同城活动推荐算法

新兴的基于活动的社交网络以活动为核心,结合线上关系与线下活动促进用户真实、有效的社交关系的形成,但过多的活动信息会使用户难以分辨和选择.结合上下文进行个性化同城活动推荐,是解决活动信息过载问题的一种有效手段.然而大部分现有的同城活动推荐算法都是从用户参与活动记录中间接统计用户对上下文信息的偏好,忽略了两者之间潜在的交叉影响关系,从而影响了推荐结果的有效性.为了解决用户参与活动偏好与上下文信息潜在交叉影响关系利用不足的问题,提出了一种基于协同上下文关系学习的同城活动推荐算法（colletivecontextual relation learning,简称CCRL）.首先,对用户参与活动记录和活动主办方、活动内容、活动地点、举办时间等相关上下文信息进行关系建模;然后,采用多关系贝叶斯个性化排序学习方法进行协同上下文关系学习及同城活动推荐.Meetup数据集上的实验结果表明,该算法在多项指标上均优于现有的主流活动推荐算法.     

Recognizing patterns in streams with imprecise timestamps

Large-scale event systems are becoming increasingly popular in a variety of domains. Event pattern evaluation plays a key role in monitoring applications in these domains. identifies matches of user-defined patterns on high-volume event streams. Existing work on pattern evaluation, however, assumes that the occurrence time of each event is known precisely and the events from various sources can be merged into a single stream with a total or partial order. We observe that in real-world applications event occurrence times are often unknown or imprecise. Therefore, we propose a temporal model that assigns a time interval to each event to represent all of its possible occurrence times and revisit pattern evaluation under this model. In particular, we propose the formal semantics of such pattern evaluation, two evaluation frameworks, and algorithms and optimizations in these frameworks. Our evaluation results using both real traces and synthetic systems show that the event-based framework always outperforms the point-based framework and with optimizations, it achieves high efficiency for a wide range of workloads tested.     

Improving the QoS for information discovery in autonomic wireless sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. In order to fully exploit the data gathering and dissemination capabilities of these networks, energy-efficient and scalable solutions for data storage and information discovery are essential. Traditionally, the communication pattern in WSNs has been assumed to be many-to-one; i.e., numerous sensors gather information which is routed to a central point commonly referred to as the sink. However, many emerging applications for WSNs require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery.In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay resulting in quality of service (QoS) improvements. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group “similar” sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time.Simulation and Analytical results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.     

wxWidgets事件处理机制

wxWidgets是一个可以在多个平台上进行图形用户界面开发的C++语言框架库。如同其它所有的GUI框架一样,wxWidgets的程序控制采用事件驱动的方式。wxWidgets主要有两种方法来处理事件:一种通过事件表宏定义静态地把事件和事件处理过程绑定在一起;另外一种通过函数动态地绑定事件和事件处理过程。本文介绍了这两种事件处理方法并对其作出分析比较,说明了动态事件处理机制代码量稍大但比静态事件表机制更加灵活方便。     

Flexible querying via if-sets

The traditional query languages used in database management systems require precise and unambiguous queries only. Fuzzy querying was introduced to relax this rigidity and allow the user more natural information retrieval. In this article we suggest how to enrich fuzzy querying by the use of IF-sets. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 587–597, 2007.     

Color plastic bistable nematic display fabricated by imprint and ink‐jet technology

Abstract— Plastic displays require new manufacturing processes and techniques to achieve acceptable cost and performance. A novel additive, low‐temperature atmospheric‐pressure self‐aligned means of fabricating integrated plastic substrates for full‐color LCDs and a bistable LC mode based on microstructure alignment are presented. By using imprinting rather than photolithographic patterning, a scalable, low‐cost manufacturing route is possible. A 2‐in.‐diagonal 128 × 128‐pixel display was made to demonstrate the principles involved, which has retained an image for in excess of 2 years.     

Asynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas

Biologically-inspired event-driven silicon retinas, so called dynamic vision sensors (DVS), allow efficient solutions for various visual perception tasks, e.g. surveillance, tracking, or motion detection. Similar to retinal photoreceptors, any perceived light intensity change in the DVS generates an event at the corresponding pixel. The DVS thereby emits a stream of spatiotemporal events to encode visually perceived objects that in contrast to conventional frame-based cameras, is largely free of redundant background information. The DVS offers multiple additional advantages, but requires the development of radically new asynchronous, event-based information processing algorithms. In this paper we present a fully event-based disparity matching algorithm for reliable 3D depth perception using a dynamic cooperative neural network. The interaction between cooperative cells applies cross-disparity uniqueness-constraints and within-disparity continuity-constraints, to asynchronously extract disparity for each new event, without any need of buffering individual events. We have investigated the algorithm’s performance in several experiments; our results demonstrate smooth disparity maps computed in a purely event-based manner, even in the scenes with temporally-overlapping stimuli.