地震前兆时序模式存储及其检索方法研究

时间序列模式在很多领域中存在,时序模式的表示及存储查询是时间序列数据挖掘的重要任务之一.分析和研究了地震前兆时序模式的特点,采用半结构化语言XML并利用分段线性表示法表示地震前兆时序模式,在此基础上提出了针对Java、PL/SQL、命令行3种不同环境下地震前兆时序模式存储及查询方法,既保证了时序模式的存储查询效率,又满足了不同平台下针对时序模式的处理,从而进一步为地震预报服务.     

Extending temporal query languages to handle imprecise time intervals

Previous work on temporal and historical databases has been mainly based on the assumption that the time intervals of temporal attributes and the start/finish points of modeled events are precisely known. In many real life situations, however, the time boundary of events and the duration of entity relationships may not be exactly known. Modeling these situations and providing a way to write queries dealing with time impreciseness represents a useful extension currently lacking in existing/proposed temporal database systems. In this paper, we discuss the problem of handling time impreciseness in temporal databases and present three models for the representation of imprecise time intervals. We illustrate the basic idea and motivation of each model, its underlying logic, tradeoffs, and important properties. We also propose query language extensions that can enrich the user interface with capabilities to formulate queries dealing with time impreciseness. Extensions to existing query constructs at both the transaction level and the operator level are presented. New operators related to time impreciseness are also presented. The models and extensions discussed in this paper enrich the flexibility of temporal databases and can be used to help users obtain more meaningful replies for their temporal queries.     

Supporting movement pattern queries in user-specified scales

An important investigation of moving objects involves searching for objects with specific movement patterns, such as "going up," "going towards southwest," or a combination of these. Movement patterns can be in various scales, and larger-scale patterns usually span over longer time periods with greater disturbances ignored. Movement pattern queries ask for moving objects which show a given movement pattern in a specific scale. This paper studies database techniques to support fast evaluation of movement pattern queries in user-specified scales. The database is assumed to contain position information of moving objects sampled at a certain time interval. A movement pattern is defined as a regular expression of movement letters where each letter describes a set of movement directions. For each series of positions, movement directions of all scales are precomputed and results are mapped into points on a plane. Points on this plane usually cluster well and can be readily bounded by trapezoids. These bounding trapezoids are then stored in a relational database and the query language SQL can be used to help evaluate movement pattern queries. This paper also reports some experiments conducted on a real data set as well as a synthesized data set. Results show that both the precomputation algorithm and the bounding strategy are efficient and scalable.     

Spatial SQL: a query and presentation language

Recently, attention has been focused on spatial databases, which combine conventional and spatially related data, such as geographic information systems, CAD/CAM, or VLSI. A language has been developed to query such spatial databases. It recognizes the significantly different requirements of spatial data handling and overcomes the inherent problems of the application of conventional database query languages. The spatial query language has been designed as a minimal extension to the interrogative part of SQL and distinguishes from previously designed SQL extensions by: the preservation of SQL concepts; the high-level treatment of spatial objects; and the incorporation of spatial operations and relationships. It consists of two components, a query language to describe what information to retrieve and a presentation language to specify how to display query results. Users can ask standard SQL queries to retrieve nonspatial data based on nonspatial constraints, use Spatial SQL commands to inquire about situations involving spatial data, and give instructions in the Graphical Presentation Language, GPL to manipulate or examine the graphical presentation     

Data mining with Temporal Abstractions: learning rules from time series

A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.     

Evaluation Techniques for Generalized Path Pattern Queries on XML Data

Finding the occurrences of structural patterns in XML data is a key operation in XML query processing. Existing algorithms for this operation focus almost exclusively on path patterns or tree patterns. Current applications of XML require querying of data whose structure is complex or is not fully known to the user, or integrating XML data sources with different structures. These applications have motivated recently the introduction of query languages that allow a partial specification of path patterns in a query. In this paper, we consider partial path queries, a generalization of path pattern queries, and we focus on their efficient evaluation under the indexed streaming evaluation model. Our approach explicitly deals with repeated labels (that is, multiple occurrences of the same label in a query). We show that partial path queries can be represented as rooted dags for which a topological ordering of the nodes exists. We present three algorithms for the efficient evaluation of these queries. The first one exploits a structural summary of data to generate a set of path patterns that together are equivalent to a partial path query. To evaluate these path patterns, we extend a previous algorithm for path-pattern queries so that it can work on path patterns with repeated labels. The second one extracts a spanning tree from the query dag, uses a stack-based algorithm to find the matches of the root-to-leaf paths in the tree, and merge-joins the matches to compute the answer. Finally, the third one exploits multiple pointers of stack entries and a topological ordering of the query dag to apply a stack-based holistic technique. We analyze our algorithms and perform extensive experimental evaluations. Our experimental results show that the holistic algorithm outperforms the other ones. Our approaches are the first ones to efficiently evaluate this class of queries in the indexed streaming model.     

Extending the UML concepts to transform natural language queries with fuzzy semantics into SQL

Database applications tend toward getting more versatile and broader to comply with the expansion of various organizations. However, naïve users usually suffer from accessing data arbitrarily by using formal query languages. Therefore, we believe that accessing databases using natural language constructs will become a popular interface in the future. The concept of object-oriented modeling makes the real world to be well represented or expressed in some kinds of logical form. Since the class diagram in UML is used to model the static relationships of databases, in this paper, we intend to study how to extend the UML class diagram representations to capture natural language queries with fuzzy semantics. By referring to the conceptual schema throughout the class diagram representation, we propose a methodology to map natural language constructs into the corresponding class diagram and employ Structured Object Model (SOM) methodology to transform the natural language queries into SQL statements for query executions. Moreover, our approach can handle queries containing vague terms specified in fuzzy modifiers, like ‘good’ or ‘bad’. By our approach, users obtain not only the query answers but also the corresponding degree of vagueness, which can be regarded as the same way we are thinking.     

时态查询语言对处理不明确时间间隔的扩展

关于时态数据库的先前工作，主要是建立在时态属性的时间间隔和事件的开始／终止点都十分清楚的基础上，然而，在许多实际情况中，事件的时间界限并不十分清楚。因此，表示不明确时间，并给出处理不明确时间查询的方法，是对时态数据库中目前仍缺少的一个有效的扩展。讨论了时态数据库处理不明确时间存在的一些问题，并描述不明确时间隔的方法，之后扩展了各种时态运算，并给出了与不明确时间相关的新运算符。也提出了能够处理不明确时间的查询语言扩展。在此讨论的时是询语言对处理不明确时间间隔的扩展和丰富时态数据库的适应性有一定作用。     

Rule-based composite event queries: the language XChangeEQ and its semantics

Web systems, Web services, and Web-based publish/subscribe systems communicate events as XML messages and in many cases, require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. This entails a need for expressive, high-level languages for querying composite events. Emphasizing language design and formal semantics, we describe the rule-based composite event query language XChange^EQ. XChange^EQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as a model theory with accompanying fixpoint theory, an approach that is established for rule languages but has not been applied to event queries so far. Because they are highly declarative, thus easy to understand and well suited for query optimization, such semantics are desirable for event queries.