Multiple query scheduling for distributed semantic caches

In distributed query processing systems, load balancing plays an important role in maximizing system throughput. When queries can leverage cached intermediate results, improving the cache hit ratio becomes as important as load balancing in query scheduling, especially when dealing with computationally expensive queries. The scheduling policies must be designed to take into consideration the dynamic contents of the distributed caching infrastructure. In this paper, we propose and discuss several distributed query scheduling policies that directly consider the available cache contents by employing distributed multidimensional indexing structures and an exponential moving average approach to predicting cache contents. These approaches are shown to produce better query plans and faster query response times than traditional scheduling policies that do not predict dynamic contents in distributed caches. We experimentally demonstrate the utility of the scheduling policies using MQO, which is a distributed, Grid-enabled, multiple query processing middleware system we developed to optimize query processing for data analysis and visualization applications.     

Identifying relevant constraints for semantic query optimization

Semantic query optimization is the process of utilizing information implied by integrity constraints to reformulate the query into one that generates the same set of answers in a more efficient way. The difficulties of identifying relevant integrity constraints for a given query have been well recognized as have the various solutions. However, most of the previous works consider the query consisting of join(s) of base relations and the integrity constraints on base relations only. We generalize these restrictions and propose a method of identifying relevant integrity constraints for queries involving any combinations of joins and unions of base and defined relations. Our method utilizes a query graph that can be constructed dynamically during the query processing time, and, as a consequence, does not rely on heavy preprocessing or normalization. The method is extended to include the use of heuristics for generating a subset of answers.     

基于语义的概念查询扩展

针对当前信息检索系统中所存在查准率低和查全率低的情况,分析了当前检索系统中常用的方法后,提出了一种基于语义的概念查询扩展方法.该方法结合概念语义空间来实现用户检索的概念查询扩展,以达到提高查准率和查全率的目的.实验结果表明,该方法相对于传统方法可以大幅提高用户检索的查准率和查全率.     

结合概念语义空间的语义扩展技术研究

查询扩展是在原查询词的基础上加入相关的词或者词组,以克服自然语言的"二义性"问题,改进查询意愿的描述。在概念语义空间中进行查询词扩展,可以充分挖掘出查询词之间的关联程度,在整体上把握查询意愿。利用WordNet语义词典中的上下文关系和相似度关系为各个原始查询词构建语义树,并将这些语义树向上溯源建立完整的概念语义空间,以共现信息为特征参数对扩展源中的词进行筛选,以避免过度扩展引起查询语义漂移。还引入动态观察窗口加权模型,以强化共现信息对单词之间关联度的表示。实验结果表明,该扩展算法比传统伪相关反馈算法的扩展质量有明显提高。     

Object-oriented query language access to relational databases: A semantic framework for query translation

This research investigates and approach to query processing in a multidatabase system that uses an objectoriented model to capture the semantics of other data models. The object-oriented model is used to construct a global schema, defining an integrated view of the different schemas in the environment. The model is also used as a self-describing model to build a meta-database for storing information about the global schema. A unique aspect of this work is that the object-oriented model is used to describe the different data models of the multidatabase environment, thereby extending the meta database with semantic information about the local schemas. With the global and local schemas all represented in an object-oriented form, structural mappings between the global schema and each local schema are then easily supported. An object algebra then provides a query language for expressing global queries, using the structural mappings to translate object algebra queries into SQL queries over local relational schema. The advantage of using an object algebra is that the object-oriented database can be viewed as a blackboard for temporary storage of local data and for establishing relationships between different databases. The object algebra can be used to directly retrieve temporarily-stored data from the object-oriented database or to transparently retrieve data from local sources using the translation process described in this paper.     

The CQL continuous query language: semantic foundations and query execution

CQL, a continuous query language, is supported by the STREAM prototype data stream management system (DSMS) at Stanford. CQL is an expressive SQL-based declarative language for registering continuous queries against streams and stored relations. We begin by presenting an abstract semantics that relies only on “black-box” mappings among streams and relations. From these mappings we define a precise and general interpretation for continuous queries. CQL is an instantiation of our abstract semantics using SQL to map from relations to relations, window specifications derived from SQL-99 to map from streams to relations, and three new operators to map from relations to streams. Most of the CQL language is operational in the STREAM system. We present the structure of CQL's query execution plans as well as details of the most important components: operators, interoperator queues, synopses, and sharing of components among multiple operators and queries. Examples throughout the paper are drawn from the Linear Road benchmark recently proposed for DSMSs. We also curate a public repository of data stream applications that includes a wide variety of queries expressed in CQL. The relative ease of capturing these applications in CQL is one indicator that the language contains an appropriate set of constructs for data stream processing. Edited by M. Franklin     

基于语义缓存的数据库查询优化研究

为了应用智能化的方法提高数据库访问效率,基于多Agent技术构建了分布式数据库访问平台,研究并解决了平台的结构、各种Agent的设计、Agent间的协作机制、以及数据库系统的包装方法等关键问题.在优化策略方面,研究了分布式环境下的语义缓存技术,并提出了一种Agent平台下的智能预取算法,弥补了传统数据库优化手段缺乏智能性、预动性,以及重用困难等不足.通过在大型数据库系统上进行测试,表明该方案在进行大规模数据库操纵时效率有明显提高.     

Reusing ontology mappings for query routing in semantic peer-to-peer environment

To efficiently support automated interoperability between ontology-based information systems in distributed environments, the semantic heterogeneity problem has to be dealt with. To do so, traditional approaches have acquired and employed explicit mappings between the corresponding ontologies. Usually these mappings can be only obtained from human domain experts. However, it is too expensive and time-consuming to collect all possible mapping results on distributed information systems. More seriously, as the number of systems in a large-scale peer-to-peer (P2P) network increases, the efficiency of the ontology mapping is exponentially decreased. Thereby, in this paper, we propose a novel semantic P2P system, which is capable of (i) sharing and exchanging existing mappings among peers, and (ii) composing shared mappings to build a certain path between two systems. Given two arbitrary peers (i.e., source and destination), the proposed system can provide indirect ontology mappings to make them interoperable. In particular, we have focused on query-based communication for evaluating the proposed ontology mapping composition system. Once direct ontology mappings are collected from candidate peers, a given query can be (i) segmented into a set of sub-queries, and (ii) transformed to another query. With respect to the precision performance, our experimentation has shown an improvement of about 42.5% compared to the keyword-based query searching method.     

Leveraging semantic resources in diversified query expansion

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. In this paper, we consider the usage of semantic resources and tools to arrive at improved methods for diversified query expansion. In particular, we develop two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively. Both the approaches operate on a common three-phase framework; that of first taking a set of informative terms from the search results of the initial query, then building a graph, following by using a diversity-conscious node ranking to prioritize candidate terms for diversified query expansion. Our methods differ in the second phase, with the first method Select-Link-Rank (SLR) linking terms with Wikipedia entities to accomplish graph construction; on the other hand, our second method, Select-Embed-Rank (SER), constructs the graph using similarities between distributional word embeddings. Through an empirical analysis and user study, we show that SLR ourperforms state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversified query expansion. Our empirical analysis also illustrates that SER outperforms the baselines convincingly, asserting that it is the best available method for those cases where SLR is not applicable; these include narrow-focus search systems where a relevant knowledge base is unavailable. Our SLR method is also seen to outperform a state-of-the-art method in the task of diversified entity ranking.     

Learning transformation rules for semantic query optimization: adata-driven approach

An approach to learning query-transformation rules based on analyzing the existing data in the database is proposed. A framework and a closure algorithm for learning rules from a given data distribution are described. The correctness, completeness, and complexity of the proposed algorithm are characterized and a detailed example is provided to illustrate the framework     

Ontology based user query interpretation for semantic multimedia contents retrieval

Users who are familiar with the existing keyword-based search have problems of not being able to configure the formal query because they don’t have generic knowledge on knowledge base when using the semantic-based retrieval system. User wants the search results which are more accurate and match the user’s search intents with the existing keyword-based search and the same search keyword without the need to recognize what technology the currently used retrieval system is based on to provide the search results. In order to do the semantic analysis of the ambiguous search keyword entered by users who are familiar with the existing keyword-based search, ontological knowledge base constructed based on refined meta-data is necessary, and the keyword semantic analysis technique which reflects user’s search intents from the well-established knowledge base and can generate accurate search results is necessary. In this paper, therefore, by limiting the knowledge base construction to multimedia contents meta-data, the applicable prototype has been implemented and its performance in the same environment as Smart TV has been evaluated. Semantic analysis of user’s search keyword is done, evaluated and recommended through the proposed ontological knowledge base framework so that accurate search results that match user’s search intents can be provided.     

Visual query interfaces for semantic datasets: An evaluation study

The rapid growth of the Linked Open Data cloud, as well as the increasing ability to lift relational enterprise datasets to a semantic, ontology-based level means that vast amounts of information are now available in a representation that closely matches the conceptualizations of the potential users of this information. This makes it interesting to create ontology based, user-oriented tools for searching and exploring this data. Although initial efforts were intended for tech users with knowledge of SPARQL/RDF, there are ongoing proposals designed for lay users. One of the most promising approaches is to use visual query interfaces, but more user studies are needed to assess their effectiveness. In this paper, we compare the effect on usability of two important paradigms for ontology-based query interfaces: form-based and graph-based interfaces. In order to reduce the number of variables affecting the comparison, we performed a user study with two state-of-the-art query tools developed by ourselves, sharing a large part of the code base: the graph-based tool OptiqueVQS*, and the form-based tool PepeSearch. We evaluated these tools in a formal comparison study with 15 participants searching a Linked Open Data version of the Norwegian Company Registry. Participants had to respond to 6 non-trivial search tasks using alternately OptiqueVQS* and PepeSearch. Even without previous training, retrieval performance and user confidence were very high, thus suggesting that both interface designs are effective for searching RDF datasets. Expert searchers had a clear preference for the graph-based interface, and mainstream searchers obtained better performance and confidence with the form-based interface. While a number of participants spontaneously praised the capability of the graph interface for composing complex queries, our results evidence that graph interfaces are difficult to grasp. In contrast, form interfaces are more learnable and relieve problems with disorientation for mainstream users. We have also observed positive results introducing faceted search and dynamic term suggestion in semantic search interfaces.     

语义缓存查询裁剪优化

查询处理是语义缓存的一个关键问题,但是现有的查询处理算法在时空效率和裁剪结果的复杂度两个方面存在很大的局限性,这在一定程度上限制了语义缓存的实用性。为了克服这些缺陷,本文对语义缓存的裁剪过程进行优化处理,减少了对服务器的无效访问,并给出了生成探测查询和剩余查询的裁剪算法;算法分析从理论上证明了该优化机制的有效性,同时,仿真实验的性能比较也表明该优化方法在提高查询裁剪时空效率和降低剩余查询复杂度等方面均要明显优于没有优化的方法。     

网格数据的语义查询优化研究

利用网格技术实现的异构数据源集成环境中,引入本体可以解决网格数据的语义查询问题。为了提高网格环境中语义查询的效率,提出了一个基于本体的语义查询优化器（GSQO）,该优化器主要实现了以下3个模块的优化：（1）用户查询语义扩展;（2）资源选择;（3）并行处理。实验结果表明,GSQO通过采取上述优化策略提供了较好的查询效率。     

Design and implementation of a semantic query optimizer

The authors describe a scheme to utilize semantic knowledge in optimizing a user-specified query. The semantics is represented as function-free clauses in predicate logic. The scheme uses a graph-theoretic approach to identify redundant joins and restrictions present in a given query while adding additional profitable specifications to it. Dynamic and heuristic interaction of three entities-schema, semantics, and query-forms the basis of the algorithm. The implementation architecture of the algorithm and test results on a representative set of data are presented. Issues associated with updating of semantic constraints are addressed, and an algorithm for semantic maintenance is introduced     

空间关键字个性化语义近似查询方法

现有的空间关键字查询处理模式大都仅支持位置相近和文本相似匹配,但不能将语义相近但形式上不匹配的对象提供给用户;并且,当前的空间-文本索引结构也不能对空间对象中的数值属性进行处理。针对上述问题,本文提出了一种支持语义近似查询的空间关键字查询方法。首先,利用词嵌入技术对用户原始查询进行扩展,生成一系列与原始查询关键字语义相关的查询关键字;然后,提出了一种能够同时支持文本和语义匹配,并利用Skyline方法对数值属性进行处理的混合索引结构AIR-Tree;最后,利用AIR-Tree进行查询匹配,返回top-k个与查询条件最为相关的有序空间对象。实验分析和结果表明,与现有同类方法相比,本文方法具有较高的执行效率和较好的用户满意度;基于AIR-Tree索引的查询效率较IRS-Tree索引提高了3.6%,在查询结果准确率上较IR-Tree和IRS-Tree索引分别提高了10.14%和16.15%。     

Graph-based query rewriting for knowledge sharing between peer ontologies

Peer knowledge management systems (PKMS) offer a flexible architecture for decentralized knowledge sharing. In PKMSs, the knowledge sharing and evolution processes are based on peer ontologies. Finding an effective and efficient query rewriting algorithm for regular expression queries is vital for knowledge sharing between peers in PKMSs; and for this our solution is characterized by graph-based query rewriting. Based on the graphs for both axioms and mappings, we design a novel algorithm, regular expression rewriting algorithm, to rewrite regular expression queries along semantic paths. The simulation results show that the performance of our algorithm is better than Mork’s reformulation algorithms [P. Mork, Peer architectures for knowledge sharing, PhD thesis, University of Washington, 2005. <http://www.mitre.org/staffpages/pmork/>], and our algorithm is more effective than the naive rewriting algorithm.     

Cluster query: a new query pattern on temporal knowledge graph

A temporal knowledge graph (TKG) is theoretically a temporal graph. Recently, systems have been developed to support snapshot queries over temporal graphs. However, snapshot queries can only give separate answers. To retrieve forward-backward correlation facts from temporal knowledge graph, cluster query is proposed in this paper. To deal with the query, the logical view and physical model are presented. Subsequently, five corresponding basic query patters of unit matching are studied, and then the complete matchings are also addressed. To improve the query performance, index-based methods and pruning strategies are adopted. Experiments are conducted to evaluate cluster queries on three real datasets. The experimental results show the effectiveness and efficiency of cluster queries on temporal knowledge graphs.

     

Enhancing electronic medical record retrieval through semantic query expansion

There are currently many active movements towards computerizing patient healthcare information. As Electronic Medical Record (EMR) systems are being increasingly adopted in healthcare facilities, however, there is a big challenge in effectively utilizing this massive information source. It is very time-consuming for healthcare providers to dig into the voluminous medical records of a patient to find the few that are indeed relevant to the patient’s current problem. Due to the complex semantic relationships among medical concepts and use of many synonyms, antonyms, and hypernym/hyponym, simple word-based information retrieval does not produce satisfactory results. In this paper, we propose an EMR retrieval system that leverages semantic query expansion to retrieve medical records that are relevant to the patient’s current symptom/problem. The proposed framework integrates various technologies, including information retrieval, domain ontologies, automatic semantic relationship learning, as well as a body of domain knowledge elicited from healthcare experts. Knowledge of semantic relationships among medical concepts, such as symptoms, exams and tests, diagnoses, and treatments, as well as knowledge of synonyms and hypernym/hyponyms, is used to expand and enhance initial queries posed by a user. We have implemented a preliminary prototype and conducted a pilot testing using sample nursing notes drawn from the EMR system of a community health center.