多时间粒度序列模式挖掘

序列模式挖掘是数据挖掘的一个重要问题.传统的序列模式仅能揭示频繁出现的项目以及出现的顺序,但不能揭示在前续项目出现的情况下,后续项目出现的时间.在本文中,引入一种新的多时间粒度序列模式,模式中相邻项目之间的转换时间采用从原数据集中导出的、多时间粒度下的最小有界时间区间和平均时间标注.建立了多时间粒度序列模式挖掘模型,提出了一种新的多时间序列模式挖掘算法MG-PrefixSpan.实验表明,算法是有效的.     

一种基于子串识别的多模式串匹配算法

多模式串匹配算法是网络内容过滤系统的核心技术。巨大的存储空间开销是制约多模式匹配串算法应用的瓶颈之一。提出一种基于子串识别的多模式匹配算法—HashBOM,该算法利用位哈希表存储模式串的子串信息以大幅度减少存储空间,利用递归哈希函数计算字符串的哈希值以实现快速匹配。理论分析表明,该算法的空间复杂度为O(rm~2),优于基于子串识别的匹配算法BOM的空间复杂度O(mr|∑|log_2mr);该算法搜索匹配过程的平均时间复杂度为O(nlog|∑|)mr/m,与BOM算法相同(其中m为最短模式串的长度,r为模式串的个数,n为待匹配文本的长度,|∑|为字母表的大小)。在随机数据集和真实数据集上的实验表明,该算法的存储空间远远低于BOM算法,而匹配速度与BOM算法相当,非常适合在线实时匹配的应用环境。     

Maintaining consistent results of continuous queries under diverse window specifications

Continuous queries applied over nonterminating data streams usually specify windows in order to obtain an evolving–yet restricted–set of tuples and thus provide timely and incremental results. Although sliding windows get frequently employed in many user requests, additional types like partitioned or landmark windows are also available in stream processing engines. In this paper, we set out to study the existence of monotonic-related semantics for a rich set of windowing constructs in order to facilitate a more efficient maintenance of their changing contents. After laying out a formal foundation for expressing windowed queries, we investigate update patterns observed in most common window variants as well as their impact on adaptations of typical operators (like windowed join, union or aggregation), thus offering more insight towards design and implementation of stream processing mechanisms. Furthermore, we identify syntactic equivalences in algebraic expressions involving windows, to the potential benefit of query optimizations. Finally, this framework is validated for several windowed operations against streaming datasets with simulations at diverse arrival rates and window specifications, providing concrete evidence of its significance.     

An approach for temporal analysis of email data based on segmentation

Many kinds of information are hidden in email data, such as the information being exchanged, the time of exchange, and the user IDs participating in the exchange. Analyzing the email data can reveal valuable information about the social networks of a single user or multiple users, the topics being discussed, and so on. In this paper, we describe a novel approach for temporally analyzing the communication patterns embedded in email data based on time series segmentation. The approach computes egocentric communication patterns of a single user, as well as sociocentric communication patterns involving multiple users. Time series segmentation is used to uncover patterns that may span multiple time points and to study how these patterns change over time. To find egocentric patterns, the email communication of a user is represented as an item-set time series. An optimal segmentation of the item-set time series is constructed, from which patterns are extracted. To find sociocentric patterns, the email data is represented as an item-setgroup time series. Patterns involving multiple users are then extracted from an optimal segmentation of the item-setgroup time series. The proposed approach is applied to the Enron email data set, which produced very promising results.     

Grounded design: Design patterns as the link between theory and practice

The process of designing a networked learning environment can be characterized by a high degree of uncertainty. This is especially true when the design includes innovative technologies. It is difficult to design a networked learning environment that closely fits the requirements of the educational practice. Often the technology seems leading in the design process because it is the most tangible aspect of the e-learning environment. This brings along the risk that the technology becomes detached from its context of use.Design patterns are a means to overcome this problem of detachment between design and practice. Design patterns make the tight relationship between the two more explicit. In this paper we further elaborate on the relationship between design and practice. We will stress the importance of theory in the development of design patterns. Theory development is an essential aspect of our approach to the development of design patterns. The approach is oriented towards a systematic analysis of problems and the development of conceptual models that guide the definition and evaluation of design patterns. In this paper we present a case study of how the approach was used to develop design patterns for networked learning in the classroom.     

An order-clique-based approach for mining maximal co-locations

Most algorithms for mining spatial co-locations adopt an Apriori-like approach to generate size-k prevalence co-locations after size-(k − 1) prevalence co-locations. However, generating and storing the co-locations and table instances is costly. A novel order-clique-based approach for mining maximal co-locations is proposed in this paper. The efficiency of the approach is achieved by two techniques: (1) the spatial neighbor relationships and the size-2 prevalence co-locations are compressed into extended prefix-tree structures, which allows the order-clique-based approach to mine candidate maximal co-locations and co-location instances; and (2) the co-location instances do not need to be stored after computing some characteristics of the corresponding co-location, which significantly reduces the execution time and space required for mining maximal co-locations. The performance study shows that the new method is efficient for mining both long and short co-location patterns, and is faster than some other methods (in particular the join-based method and the join-less method).     

Combining appearance and motion for face and gender recognition from videos

While many works consider moving faces only as collections of frames and apply still image-based methods, recent developments indicate that excellent results can be obtained using texture-based spatiotemporal representations for describing and analyzing faces in videos. Inspired by the psychophysical findings which state that facial movements can provide valuable information to face analysis, and also by our recent success in using LBP (local binary patterns) for combining appearance and motion for dynamic texture analysis, this paper investigates the combination of facial appearance (the shape of the face) and motion (the way a person is talking and moving his/her facial features) for face analysis in videos. We propose and study an approach for spatiotemporal face and gender recognition from videos using an extended set of volume LBP features and a boosting scheme. We experiment with several publicly available video face databases and consider different benchmark methods for comparison. Our extensive experimental analysis clearly assesses the promising performance of the LBP-based spatiotemporal representations for describing and analyzing faces in videos.     

Design patterns for user interface for mobile applications

The topic of this paper is a collection of user interface (UI) design patterns for mobile applications. In the paper we present the structure of the patterns collection – the patterns are suggested solutions to problems that are grouped into a set of problem areas that are further grouped into three main problem areas – a structure which is valuable both as an index to identifying patterns to use, and it gives a fairly comprehensive overview of issues when designing user interfaces for mobile applications. To show the breadth of the patterns collection we present six individual problems with connected design patterns in some detail – each coming from different problem areas. They represent important and relevant problems, and are on different levels of abstraction, thus showing how patterns may be used to present problems and solutions on different levels of detail. To show the relevance and usefulness of the patterns collection for usability professionals with a mixed background, we present some relevant findings from a validation of the patterns collection. In addition to verifying the relevance and usefulness of the patterns collection, it also shows both expected and surprising correlations between background and perceived relevance and usefulness. One important finding from the validation is an indication that the patterns collection is best suited for experienced UI developers wanting to start developing mobile UIs. Using a patterns collection for documenting design knowledge and experience has been a mixed experience, so we discuss pros and cons of this. Finally, we present related work and future research.     

Discovery of stop regions for understanding repeat travel behaviors of moving objects

GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method.     

谈话节目语料库的构建与会话结构分析

口语语料库的建设是口语研究的基础工作,该文选择具有代表性的交谈式谈话节目《锵锵三人行》和对谈式谈话节目《鲁豫有约》作为语料,建立了一个小型的谈话节目语料库,并构建了包含五大类16小类的会话结构标注体系,对语料进行了会话结构的标注。统计得到打断结构309例,插入结构141例,重复结构111例,问答结构653/589例,阻碍—修正结构51/21例,反映了会话结构在数量上的不均衡分布,节目的形式、性质以及交际任务是会话结构分布的主要影响因素。会话结构组合具有模式性,该文使用Trigram方法对其组合情况进行了分析,发现语料中的高频组合是问答毗邻对,此外有大量的非毗邻性组合。会话结构组合模式不但反映出谈话节目的风格特点,还有助于分析会话中的功能性模块、会话策略的形成,进而更加深入地了解会话的运作机制。