Temporal Coupling Verification in Time Series Databases

Time series are often generated by continuous sampling or measurement of natural or social phenomena. In many cases, events cannot be represented by individual records, but instead must be represented by time series segments (temporal intervals). A consequence of this segment-based approach is that the analysis of events is reduced to analysis of occurrences of time series patterns that match segments representing the events.A major obstacle on the path toward event analysis is the lack of query languages for expressing interesting time series patterns. We have introduced SQL/LPP (Perng and Parker, 1999). Which provides fairly strong expressive power for time series pattern queries, and are now able to attack the problem of specifying queries that analyze temporal coupling, i.e., temporal relationships obeyed by occurrences of two or more patterns.In this paper, we propose SQL/LPP+, a temporal coupling verification language for time series databases. Based on the pattern definition language of SQL/LPP (Perng and Parker, 1999), SQL/LPP+ enables users to specify a query that looks for occurrences of a cascade of multiple patterns using one or more of Allen's temporal relationships (Allen, 1983) and obtain desired aggregates or meta-aggregates of the composition. Issues of pattern composition control are also discussed.     

基于时间不确定性的HLA乐观时间同步

在现有仿真时间推进机制中，事件的发生时间采用精确的时间戳，但是有时事件的发生时间用一个时间区间来表达更合理。该文提供了一种新的时间卷曲方法来发掘事件发生时间的不确定性，此方法的核心思想是：用事件的时间区间模型代替精确时间戳模型来提高事件并发性；当事件的时间区间重叠时用因果关系防止失序；对因果关系事件采用语义学方法进行区分，防止过多回滚。在RTI上增加时间管理扩展组件，用来实现基于时间区间的乐观时间同步机制。     

Temporal event searches based on event maps and relationships

To satisfy a user’s need to find and understand the whole picture of an event effectively and efficiently, in this paper we formalize the problem of temporal event searches and propose a framework of event relationship analysis for search events based on user queries. We define three kinds of event relationships: temporal, content dependence, and event reference, that can be used to identify to what extent a component event is dependent on another in the evolution of a target event (i.e., the query event). The search results are organized as a temporal event map (TEM) that serves as the whole picture about an event’s evolution or development by showing the dependence relationships among events. Based on the event relationships in the TEM, we further propose a method to measure the degrees of importance of events, so as to discover the important component events for a query, as well as the several algebraic operators involved in the TEM, that allow users to view the target event. Experiments conducted on a real data set show that our method outperforms the baseline method Event Evolution Graph (EEG), and it can help discover certain new relationships missed by previous methods and even by human annotators.     

SellTrend: Inter-Attribute Visual Analysis of Temporal Transaction Data

We present a case study of our experience designing SellTrend, a visualization system for analyzing airline travel purchase requests. The relevant transaction data can be characterized as multi-variate temporal and categorical event sequences, and the chief problem addressed is how to help company analysts identify complex combinations of transaction attributes that contribute to failed purchase requests. SellTrend combines a diverse set of techniques ranging from time series visualization to faceted browsing and historical trend analysis in order to help analysts make sense of the data. We believe that the combination of views and interaction capabilities in SellTrend provides an innovative approach to this problem and to other similar types of multivariate, temporally driven transaction data analysis. Initial feedback from company analysts confirms the utility and benefits of the system.     

LOCALIZED TEMPORAL REASONING USING SUBGOALS AND ABSTRACT EVENTS

We are concerned with temporal reasoning problems where there is uncertainty about the order in which events occur. The task of temporal reasoning is to derive an event sequence consistent with a given set of ordering constraints to achieve a goal. Previous research shows that the associated decision problems are hard even for very restricted cases. In this article, we investigate locality in event ordering and causal dependencies. We present a localized temporal reasoning algorithm that uses subgoals and abstract events to exploit locality. The computational efficiency of our algorithm for a problem instance is quantified by the inherent locality in the instance. We theoretically demonstrate the substantial improvement in performance gained by exploiting locality. This work provides solid evidence of the usefulness of localized reasoning in exploiting locality.     

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     

Temporal XML: modeling, indexing, and query processing

In this paper we address the problem of modeling and implementing temporal data in XML. We propose a data model for tracking historical information in an XML document and for recovering the state of the document as of any given time. We study the temporal constraints imposed by the data model, and present algorithms for validating a temporal XML document against these constraints, along with methods for fixing inconsistent documents. In addition, we discuss different ways of mapping the abstract representation into a temporal XML document, and introduce TXPath, a temporal XML query language that extends XPath 2.0. In the second part of the paper, we present our approach for summarizing and indexing temporal XML documents. In particular we show that by indexing continuous paths, i.e., paths that are valid continuously during a certain interval in a temporal XML graph, we can dramatically increase query performance. To achieve this, we introduce a new class of summaries, denoted TSummary, that adds the time dimension to the well-known path summarization schemes. Within this framework, we present two new summaries: LCP and Interval summaries. The indexing scheme, denoted TempIndex, integrates these summaries with additional data structures. We give a query processing strategy based on TempIndex and a type of ancestor-descendant encoding, denoted temporal interval encoding. We present a persistent implementation of TempIndex, and a comparison against a system based on a non-temporal path index, and one based on DOM. Finally, we sketch a language for updates, and show that the cost of updating the index is compatible with real-world requirements.     

Exploiting Temporal Relations in Mining Hepatitis Data

Various data mining methods have been developed last few years for hepatitis study using a large temporal and relational database given to the research community. In this work we introduce a novel temporal abstraction method to this study by detecting and exploiting temporal patterns and relations between events in viral hepatitis such as “event A slightly happened before event B and B simultaneously ended with event C”. We developed algorithms to first detect significant temporal patterns in temporal sequences and then to identify temporal relations between these temporal patterns. Many findings by data mining methods applied to transactions/graphs of temporal relations shown to be significant by physician evaluation and matching with published in Medline.     

Development of a Temporal Extension to Query Travel Behavior Time Paths Using an Object-Oriented GIS

An essential requirement to better understand activity-based travel behavior (ABTB) at the disaggregate level is the development of a spatio-temporal model able to support queries related to activities of individuals or groups of individuals. This paper describes the development and implementation of a temporal extension to a geographic information system (GIS) object-oriented model for the modeling of the time path and the retrieval of its event chaining. In this approach, time path is formulated as a totally time ordered set composed by activity events and trip events, themselves organized into time ordered sets. As sets, the time path and its components can be searched using their respective indexes. A series of methods were built that implement temporal predicates as an interface to temporally query the database. A set of positional operator methods were also designed that transform temporal topological queries into retrieval functions based on set ordering indices. Taken together, the temporal predicates and the positional operator methods define a temporal query extension that meets the retrieval needs of an ABTB database.     

InforSIB中的复合事件时间模型

复合事件处理通过分析多个事件类型实例之间的关系以产生对应用感兴趣的复合事件.事件处理中已有的时间模型或者使用点时间戳建模原子和复合事件,或者定义的复合事件时间戳考虑不周,导致复合事件检测与复合事件语义存在不一致的结果;另外,需要根据应用需求对时间模型的准确性与复合事件的检测效率作出权衡.针对这两个问题,在面向服务计算平台InforSIB中定义了复合事件时间模型,包括复合事件时间戳和事件不同步与传输延迟的解决方案,最后基于时间模型给出了相应的高效的复合事件检测算法.实验结果证明了时间模型的有效性.     

基于时间分布特征的博客突发事件检测

博客是目前网络舆论的重要载体之一,如何自动检测博客中的突发事件对于舆情分析与疏导具有重要的研究价值。针对目前突发事件检测中存在的时间信息有歧义的虚假突发事件问题,本文提出了一种基于时间分布特征的博客突发事件检测方法。该方法通过波峰检测和计算事件文档与背景语料文档之间、事件相关文档和不相关文档之间的时间分布差异来判断该事件在时间特征上是否具有突发性和关联性。实验结果表明,该方法可有效检测博客中的突发事件并可有效去除时间信息有歧义的虚假突发事件。     

Temporal Organization in a Repetitive Speed Skill

The investigation sought to recognize the strategies of effector organization used by the human operator, from a detailed multi-level analysis of the response in a repetitive speed skill, hand cranking. In the experiment motion photography, a strain gauge record of the force exerted on the handle of the crank, and an electromyographic analysis of the activity of the principal agonist and antagonist muscles about the wrist, elbow and shoulder joints of the arm were employed to record the details of the on-going response.

Effector organization was evaluated in terms of serial timing and positional timing. The serial timing of the response units involves (i) the sequencing or ordering of these units and (ii) the phasing or temporal structuring of these ordered response units. Positional timing is the relationship between the occurrence of the response and an external event or signal.

The analysis of the results showed that phasing is of central importance in the organization of the response by effector processes. This form of temporal structuring appears to be more significant than positional timing. There was some evidence that the faster subjects were also the more consistent in the temporal organization of the response. It is suggested that the skilled performer establishes a relatively stable motor programme in which the instructions initiating the principal events are ordered into a well defined temporal sequence which operates in part in an open-loop fashion.     

Similarity-Profiled Temporal Association Mining

Given a time stamped transaction database and a user-defined reference sequence of interest over time, similarity-profiled temporal association mining discovers all associated item sets whose prevalence variations over time are similar to the reference sequence. The similar temporal association patterns can reveal interesting relationships of data items which co-occur with a particular event over time. Most works in temporal association mining have focused on capturing special temporal regulation patterns such as cyclic patterns and calendar scheme-based patterns. However, our model is flexible in representing interesting temporal patterns using a user-defined reference sequence. The dissimilarity degree of the sequence of support values of an item set to the reference sequence is used to capture how well its temporal prevalence variation matches the reference pattern. By exploiting interesting properties such as an envelope of support time sequence and a lower bounding distance for early pruning candidate item sets, we develop an algorithm for effectively mining similarity-profiled temporal association patterns. We prove the algorithm is correct and complete in the mining results and provide the computational analysis. Experimental results on real data as well as synthetic data show that the proposed algorithm is more efficient than a sequential method using a traditional support-pruning scheme.     

Handling of Alternatives and Events in Temporal Databases

Planning for the future is an important activity both at the individual and organizational levels. Planning consists of defining alternative actions to handle various events in the future. The alternatives arise becau]se of different possible outcomes of events. A plan consists of a sequence of actions to be carried out for each possible outcome. In the context of database modeling, the actions are operations on a database. A database management system should enable its users to define events and alternatives, and also allow them to interact with the database under different alternatives (possibly to evaluate different plans). The existing temporal data models treat the future analogous to the past or present; they provide for one future path (in the sense that facts valid at some future time can be stored), but do not provide support for alternatives in the future. In this paper, we present a model for incorporating events and alternatives by extending the temporal data model to support branching time. The extended model permits definitions of events, their interdependencies and associated actions. The events that affect an object are modeled by a tree, permitting an object to have different states at the same valid time but under different alternatives. The branching time paradigm is obtained by superimposing a linear valid time on the event tree. We extend the temporal relational algebra and the Temporal SQL2 to support a branching time data model. The paper also briefly deals with the uncertainties associated with future planning as well as probabilities of possible event outcomes. Finally, we sketch an implementation strategy for the branching time data model.     

TMS-RFID: Temporal management of large-scale RFID applications

In coming years, there will be billions of RFID tags living in the world tagging almost everything for tracking and identification purposes. This phenomenon will impose a new challenge not only to the network capacity but also to the scalability of event processing of RFID applications. Since most RFID applications are time sensitive, we propose a notion of Time To Live (TTL), representing the period of time that an RFID event can legally live in an RFID data management system, to manage various temporal event patterns. TTL is critical in the “Internet of Things” for handling a tremendous amount of partial event-tracking results. Also, TTL can be used to provide prompt responses to time-critical events so that the RFID data streams can be handled timely. We divide TTL into four categories according to the general event-handling patterns. Moreover, to extract event sequence from an unordered event stream correctly and handle TTL constrained event sequence effectively, we design a new data structure, namely Double Level Sequence Instance List (DLSIList), to record intermediate stages of event sequences. On the basis of this, an RFID data management system, namely Temporal Management System over RFID data streams (TMS-RFID), has been developed. This system can be constructed as a stand-alone middleware component to manage temporal event patterns. We demonstrate the effectiveness of TMS-RFID on extracting complex temporal event patterns through a detailed performance study using a range of high-speed data streams and various queries. The results show that TMS-RFID has a very high throughput, namely 170,000–870,000 events per second for different highly complex continuous queries. Moreover, the experiments also show that the main structure to record the intermediate stages in TMS-RFID does not increase exponentially with the number of events. These results demonstrate that TMS-RFID not only supports high processing speeds, but is also highly scalable.     

一类具有时态约束的关联规则的知识发现问题

本文探讨了一类具有时态约束的关联规则的有关问题。首先,我们引入了时态型的定义以及相关的概念。接下来,我们给出了一种时态事件模型,用以描述基于时态型的不同属性的各种状态的事件,也定义了一类具有时态约束的关联规则,它能在诸如股票波动、天气预报、商品销售等领域提供短期的预测和决策。最后,我们给出了发现具有时态约束的关联规则算法的主要步骤。     

Fuzzifying Allen's Temporal Interval Relations

When the time span of an event is imprecise, it can be represented by a fuzzy set, called a fuzzy time interval. In this paper, we propose a framework to represent, compute, and reason about temporal relationships between such events. Since our model is based on fuzzy orderings of time points, it is not only suitable to express precise relationships between imprecise events (ldquoRoosevelt died before the beginning of the Cold Warrdquo) but also imprecise relationships (ldquoRoosevelt died just before the beginning of the Cold Warrdquo). We show that, unlike previous models, our model is a generalization that preserves many of the properties of the 13 relations Allen introduced for crisp time intervals. Furthermore, we show how our model can be used for efficient fuzzy temporal reasoning by means of a transitivity table. Finally, we illustrate its use in the context of question answering systems.     

时空尺度下域间路由事件的定位方法

针对现有的定位方法无法在时间演化尺度下对域间路由事件进行有效定位的问题,提出了一种时空尺度下的域间路由事件定位方法。从BGP路由表中提取事件在不同时刻下AS级网络的可达性特征和连通性特征,在此基础上计算相邻时刻的时序距离,并将距离最大者推断为事件触发源所在的时间窗口;对该窗口内的AS级网络进行遍历,将具有最高召回率和精度的网络元素识别为事件的触发源。分别以网络瘫痪事件和前缀劫持事件进行实验验证,结果表明,该方法能够准确推断不同类型域间路由事件的起始时间和触发源。     

A Temporal Logic for Supporting Historical Databases

Tackling data with gap-interval time is an important issue faced by the temporal database community. While a number of interval logics have been developed, less work has been reported on gap-interval time. To represent and handle data with time, a clause ‘when’ is generally added into each conventional operator so as to incorporate time dimension in temporal databases, which clause ‘when’ is really a temporal logical sentence. Unfortunately, though several temporal database models have dealt with data with gap-interval time, they still put interval calculus methods on gap-intervals. Certainly, it is inadequate to tackle data with gap-interval time using interval calculus methods in historical databases. Consequently, what temporal expressions are valid in the clause ‘when’ for tackling data with gap-interval time? Further, what temporal operations and relations can be used in the clause ‘when’? To solve these problems, a formal tool for supporting data with gap-interval time must be explored. For this reason, a gap-interval-based logic for historical databases is established in this paper. In particular, we discuss how to determine the temporal relationships after an event explodes. This can be used to describe the temporal forms of tuples splitting in historical databases. Received 2 February 1999 / Revised 9 May 1999 / Accepted in revised form 20 November 1999