Mining generalized temporal patterns based on fuzzy counting

Event-based sequences are a kind of pattern based on temporal associations with two essential characteristics: they are syntactically simple and have a great expressive power. For this reason, event-based sequence mining is an interesting solution to the problem of knowledge discovery in dynamic domains, mainly characterized by a time-varying nature. The inter-transactional model has led to the design of algorithms aimed to obtain this sort of patterns from time-stamped datasets. These algorithms extend the well-known Apriori algorithm, by explicitly adding the temporal context where associations among frequent events occurs. This leads to the possibility of extracting a larger number of patterns with a potential interest in decision making. However, its usefulness is diminished in those datasets where the characteristics of variability and uncertainty are present, which is a common issue in real domains. This is due to the rigidity of the counting method, which uses an exact measure of distance between temporal events. As a solution, we propose a generalization of the temporal mining process, which implies a relaxation of the counting method including the concept of approximate temporal distance between events. In particular, in this paper we present an algorithm, called TSET^fuzzy-Miner, which incorporates a fuzzy-based counting technique in order to extract general, flexible, and practical temporal patterns taking into account the particular characteristics of real domains.     

A new approach to discover interlacing data structures in high-dimensional space

The discovery of structures hidden in high-dimensional data space is of great significance for understanding and further processing of the data. Real world datasets are often composed of multiple low dimensional patterns, the interlacement of which may impede our ability to understand the distribution rule of the data. Few of the existing methods focus on the detection and extraction of the manifolds representing distinct patterns. Inspired by the nonlinear dimensionality reduction method ISOmap, in this paper we present a novel approach called Multi-Manifold Partition to identify the interlacing low dimensional patterns. The algorithm has three steps: first a neighborhood graph is built to capture the intrinsic topological structure of the input data, then the dimensional uniformity of neighboring nodes is analyzed to discover the segments of patterns, finally the segments which are possibly from the same low-dimensional structure are combined to obtain a global representation of distribution rules. Experiments on synthetic data as well as real problems are reported. The results show that this new approach to exploratory data analysis is effective and may enhance our understanding of the data distribution.     

Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events

Previous sequential pattern mining studies have dealt with either point-based event sequences or interval-based event sequences. In some applications, however, event sequences may contain both point-based and interval-based events. These sequences are called hybrid event sequences. Since the relationships among both kinds of events are more diversiform, the information obtained by discovering patterns from these events is more informative. In this study we introduce a hybrid temporal pattern mining problem and develop an algorithm to discover hybrid temporal patterns from hybrid event sequences. We carry out an experiment using both synthetic and real stock price data to compare our algorithm with the traditional algorithms designed exclusively for mining point-based patterns or interval-based patterns. The experimental results indicate that the efficiency of our algorithm is satisfactory. In addition, the experiment also shows that the predicting power of hybrid temporal patterns is higher than that of point-based or interval-based patterns.     

基于感知重采样和多模态融合的连续情感识别

情感识别在人机交互中发挥着重要的作用,连续情感识别因其能检测到更广泛更细微的情感而备受关注。在多模态连续情感识别中,针对现有方法获取的时序信息包含较多冗余以及多模态交互信息捕捉不全面的问题,提出基于感知重采样和多模态融合的连续情感识别方法。首先感知重采样模块通过非对称交叉注意力机制去除模态冗余信息,将包含时序关系的关键特征压缩到隐藏向量中,降低后期融合的计算复杂度。其次多模态融合模块通过交叉注意力机制捕捉模态间的互补信息,并利用自注意力机制获取模态内的隐藏信息,使特征信息更丰富全面。在Ulm-TSST和Aff-Wild2数据集上唤醒度和愉悦度的CCC均值分别为63.62%和50.09%,证明了该模型的有效性。     

软件交互行为的可信性分析与态势预测研究

研究现代分布式软件系统中交互实体的行为可信性问题,关注运行期意图、情景、行为和行为效应之间的关系,采用先进的统计机器学习工具分析行为踪迹规律,提出了一个新的软件行为分析与态势预测方法.针对松散聚合的交互实体间可能产生新的交互事件和行为模式的问题,本文用分层Dirichlet过程和无限隐Markov模型对被监测的交互接口数据进行聚类确定未知交互事件,用含有未知事件的序列进行行为模式的半监督学习,由管理者将其添加到规则与知识库中.在确定未知事件和行为模式时,用Beam抽样方法较其他方法(如Gibbs抽样)有更高的数据抽样和推理效率.当知识库的行为模式达到一定规模时,系统便可以无监督地对交互行为进行分析和预测.本文用HMM的Viterbi算法分析当前交互事件的最佳序列,从而确定当前交互行为的善恶,对恶意行为及时报警,对非恶意行为的后续趋势进行主动预测.通过仿真实验证实了该方法在软件行为分析与预测上具有独特的优势.     

Effective periodic pattern mining in time series databases

The goal of analyzing a time series database is to find whether and how frequent a periodic pattern is repeated within the series. Periodic pattern mining is the problem that regards temporal regularity. However, most of the existing algorithms have a major limitation in mining interesting patterns of users interest, that is, they can mine patterns of specific length with all the events sequentially one after another in exact positions within this pattern. Though there are certain scenarios where a pattern can be flexible, that is, it may be interesting and can be mined by neglecting any number of unimportant events in between important events with variable length of the pattern. Moreover, existing algorithms can detect only specific type of periodicity in various time series databases and require the interaction from user to determine periodicity. In this paper, we have proposed an algorithm for the periodic pattern mining in time series databases which does not rely on the user for the period value or period type of the pattern and can detect all types of periodic patterns at the same time, indeed these flexibilities are missing in existing algorithms. The proposed algorithm facilitates the user to generate different kinds of patterns by skipping intermediate events in a time series database and find out the periodicity of the patterns within the database. It is an improvement over the generating pattern using suffix tree, because suffix tree based algorithms have weakness in this particular area of pattern generation. Comparing with the existing algorithms, the proposed algorithm improves generating different kinds of interesting patterns and detects whether the generated pattern is periodic or not. We have tested the performance of our algorithm on both synthetic and real life data from different domains and found a large number of interesting event sequences which were missing in existing algorithms and the proposed algorithm was efficient enough in generating and detecting periodicity of flexible patterns on both types of data.     

Discovering multi-label temporal patterns in sequence databases

Sequential pattern mining is one of the most important data mining techniques. Previous research on mining sequential patterns discovered patterns from point-based event data, interval-based event data, and hybrid event data. In many real life applications, however, an event may involve many statuses; it might not occur only at one certain point in time or over a period of time. In this work, we propose a generalized representation of temporal events. We treat events as multi-label events with many statuses, and introduce an algorithm called MLTPM to discover multi-label temporal patterns from temporal databases. The experimental results show that the efficiency and scalability of the MLTPM algorithm are satisfactory. We also discuss interesting multi-label temporal patterns discovered when MLTPM was applied to historical Nasdaq data.     

Efficient temporal mining of micro-blog texts and its application to event discovery

In this paper we present a novel method for clustering words in micro-blogs, based on the similarity of the related temporal series. Our technique, named SAX*, uses the Symbolic Aggregate ApproXimation algorithm to discretize the temporal series of terms into a small set of levels, leading to a string for each. We then define a subset of “interesting” strings, i.e. those representing patterns of collective attention. Sliding temporal windows are used to detect co-occurring clusters of tokens with the same or similar string. To assess the performance of the method we first tune the model parameters on a 2-month 1 % Twitter stream, during which a number of world-wide events of differing type and duration (sports, politics, disasters, health, and celebrities) occurred. Then, we evaluate the quality of all discovered events in a 1-year stream, “googling” with the most frequent cluster n-grams and manually assessing how many clusters correspond to published news in the same temporal slot. Finally, we perform a complexity evaluation and we compare SAX* with three alternative methods for event discovery. Our evaluation shows that SAX* is at least one order of magnitude less complex than other temporal and non-temporal approaches to micro-blog clustering.     

位域实时视频水印算法

在目前数字水印算法中，图像水印的算法远多于视频水印，然而在现实生活中，视频产品保护更加重要．视频水印除了满足图像水印的一般特性之外，还必须满足实时特性．因此视频水印的嵌入和提取算法必须有较低的计算复杂性．本文提出一种用于视频产品完整性认证的位域实时视频水印的算法．实验表明，该算法有较高的视觉质量，同时能对修改进行准确定位．满足完整性认证水印要求．     

Real‐Time Gesture–Based Communication Using Possibility Theory–Based Hidden Markov Model

Exploring correct patterns from low‐frequency time‐series data is challenging. For resolving this problem, the concept of possibility theory–based hidden Markov model (PTBHMM) has been proposed. In this article, all three fundamental problems (evaluation, decoding, and learning) of conventional HMM have been addressed using possibility theory. For handling uncertainty, we have used an axiomatic approach of possibility theory proposed by Zadeh. The time complexity of existing solutions of HMM (forward, backward, Viterbi, and Baum Welch) and proposed possibility‐based solutions has been calculated and compared. From the comparison result, it has been found that PTBHMM has lesser time complexity and hence will be more suitable for real‐time gesture–based communication.     

Space‐in‐Time and Time‐in‐Space Self‐Organizing Maps for Exploring Spatiotemporal Patterns

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post‐processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two‐dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA.     

Mining Nonambiguous Temporal Patterns for Interval-Based Events

Previous research on mining sequential patterns mainly focused on discovering patterns from point-based event data. Little effort has been put toward mining patterns from interval-based event data, where a pair of time values is associated with each event. Kam and Fu's work in 2000 identified 13 temporal relationships between two intervals. According to these temporal relationships, a new variant of temporal patterns was defined for interval-based event data. Unfortunately, the patterns defined in this manner are ambiguous, which means that the temporal relationships among events cannot be correctly represented in temporal patterns. To resolve this problem, we first define a new kind of nonambiguous temporal pattern for interval-based event data. Then, the TPrefixSpan algorithm is developed to mine the new temporal patterns from interval-based events. The completeness and accuracy of the results are also proven. The experimental results show that the efficiency and scalability of the TPrefixSpan algorithm are satisfactory. Furthermore, to show the applicability and effectiveness of temporal pattern mining, we execute experiments to discover temporal patterns from historical Nasdaq data     

A Signature-Based Indexing Method for Efficient Content-Based Retrieval of Relative Temporal Patterns

A number of algorithms have been proposed for the discovery of temporal patterns. However, since the number of generated patterns can be large, selecting which patterns to analyze can be nontrivial. There is thus a need for algorithms and tools that can assist in the selection of discovered patterns so that subsequent analysis can be performed in an efficient and, ideally, interactive manner. In this paper, we propose a signature-based indexing method to optimize the storage and retrieval of a large collection of relative temporal patterns.     

关系数据库中社区发现方法研究

文中在研究了现有社区发现算法的基础上,提出了一种简单的加权网络中社区发现方法。文中基于社区结构最为普遍的性质,受社会网络中真实社区结构和并行计算的任务划分规则的启发,提出了基于核心边的加权网络中社区发现方法。该方法首先依据网络中边的权值寻找核心边;然后依据相似性度量,发现网络中的一个初始社区;最后通过隶属度度量,将发现的初始社区逐步扩展成网络中的社区结构。该方法在进行社区结构发现的过程中,仅仅依赖节点所处位置的局部信息,可以在对网络进行广度优先遍历的过程中完成社区发现工作。因此该方法具有较低的计算复杂度,可以适用于大规模网络中的社区发现。通过有效性实验和效率实验,表明该方法可以有效发现大规模网络中的社区结构。     

Context Aware Terrain Visualization for Wayfinding and Navigation

To assist wayfinding and navigation, the display of maps and driving directions on mobile devices is nowadays commonplace. While existing system can naturally exploit GPS information to facilitate orientation, the inherently limited screen space is often perceived as a drawback compared to traditional street maps as it constrains the perception of contextual information. Moreover, occlusion issues add to this problem if the environment is shown from the popular egocentric perspective. In this paper we describe an interactive visualization system that addresses these problems by reallocating the available screen space. At the heart of our system are three novel visualization techniques: First, we propose a non‐standard perspective that allows to blend between the familiar pedestrian perspective and a standard map depiction with reduced occlusion. Second, we derive an efficient deformation technique that allows an interactive allocation of screen space to areas of interest like e.g. nearby touristic attractions. Finally, a path adaptive isometric perspective is proposed that reveals otherwise hidden facades in top‐down views. We describe efficient implementations of all techniques and exemplify our interactive system on real world urban models.     

ATSP:时态网络可视化的自适应时间片划分方法

现有的时态网络可视化方法大多采用等量时间片来可视化网络的演变，不利于时态模式的快速挖掘和发现。为此，根据时态网络固有的特征提出自适应时间片划分方法（Adaptive Time Slice Partition method，ATSP）。在时态网络的两种表示方式（基于事件的表示方式和基于快照的表示方式）的基础上，构建了ATSP的基础模型，同时提出了一种改进模型用来描述事件间隔时间服从长尾分布的时态网络。为了实现时间片的不等量划分，针对探索任务的不同提出了基于时态模式的ATSP规则和基于中心节点的ATSP规则，并提出了实现算法--层次划分算法（Hierarchical Partition algorithm，HP）和增量划分算法（Incremental Partition algorithm，IP）。实验结果表明，ATSP方法比传统的时间片划分方法更能准确地表示网络的时态特征，且该方法应用于可视化时，能有效归纳并展示网络的特征，明显提高了视觉分析的效率。     

Temporal reasoning about fuzzy intervals

Traditional approaches to temporal reasoning assume that time periods and time spans of events can be accurately represented as intervals. Real-world time periods and events, on the other hand, are often characterized by vague temporal boundaries, requiring appropriate generalizations of existing formalisms. This paper presents a framework for reasoning about qualitative and metric temporal relations between vague time periods. In particular, we show how several interesting problems, like consistency and entailment checking, can be reduced to reasoning tasks in existing temporal reasoning frameworks. We furthermore demonstrate that all reasoning tasks of interest are NP-complete, which reveals that adding vagueness to temporal reasoning does not increase its computational complexity. To support efficient reasoning, a large tractable subfragment is identified, among others, generalizing the well-known ORD Horn subfragment of the Interval Algebra (extended with metric constraints).     

Interactive Fusion and Tracking For Multi‐Modal Spatial Data Visualization

Scientific data acquired through sensors which monitor natural phenomena, as well as simulation data that imitate time‐identified events, have fueled the need for interactive techniques to successfully analyze and understand trends and patterns across space and time. We present a novel interactive visualization technique that fuses ground truth measurements with simulation results in real‐time to support the continuous tracking and analysis of spatiotemporal patterns. We start by constructing a reference model which densely represents the expected temporal behavior, and then use GPU parallelism to advect measurements on the model and track their location at any given point in time. Our results show that users can interactively fill the spatio‐temporal gaps in real world observations, and generate animations that accurately describe physical phenomena.     

基于Sequitur的时间序列异步周期模式挖掘

现有的时间序列异步周期模式挖掘方法是在获取1-pattern有效段及周期的基础上再以枚举法得到i-patterns,时间复杂度较高。为解决该问题,提出一种改进的异步周期模式挖掘方法。在时间序列符号化后,使用基于Sequitur的候选模式算法获取候选i-patterns及其事件位置序列,通过基于OEOP的i-patterns有效段生成算法得到1-pattern和i-patterns的有效段及周期,从而生成有效子序列。实验结果表明,该方法具有较高的挖掘效率。