An efficient lattice-based phonetic search method for accelerating keyword spotting in large speech databases

This paper describes an algorithm for the reduction of computational complexity in phonetic search KeyWord Spotting (KWS). This reduction is particularly important when searching for keywords within very large speech databases and aiming for rapid response time. The suggested algorithm consists of an anchor-based phoneme search that reduces the search space by generating hypotheses only around phonemes recognized with high reliability. Three databases have been used for the evaluation: IBM Voicemail I and Voicemail II, consisting of long spontaneous utterances and the Wall Street Journal portion of the MACROPHONE database, consisting of read speech utterances. The results indicated a significant reduction of nearly 90 % in the computational complexity of the search while improving the false alarm rate, with only a small decrease in the detection rate in both databases. Search space reduction, as well as, performance gain or loss can be controlled according to the user preferences via the suggested algorithm parameters and thresholds.     

An effective 3-in-1 keyword search method over heterogeneous data sources

Conventional keyword search engines are restricted to a given data model and cannot easily adapt to unstructured, semi-structured or structured data. In this paper, we propose an efficient and adaptive keyword search method, called EASE, for indexing and querying large collections of heterogeneous data. To achieve high efficiency in processing keyword queries, we first model unstructured, semi-structured and structured data as graphs, and then summarize the graphs and construct graph indices instead of using traditional inverted indices. We propose an extended inverted index to facilitate keyword-based search, and present a novel ranking mechanism for enhancing search effectiveness. We have conducted an extensive experimental study using real datasets, and the results show that EASE achieves both high search efficiency and high accuracy, and outperforms the existing approaches significantly.     

基于关键字的数据库搜索研究综述

介绍了基于关键字的关系数据库搜索技术的研究成果,从数据建模、体系结构、关键算法等几个方面详细分析和比较了各种技术的特点和优劣,并指出了现有技术中存在的一些问题,提出数据库关键字搜索技术未来的研究方向。     

Aggregate keyword search on large relational databases

Keyword search has been recently extended to relational databases to retrieve information from text-rich attributes. However, all the existing methods focus on finding individual tuples matching a set of query keywords from one table or the join of multiple tables. In this paper, we motivate a novel problem of aggregate keyword search: finding minimal group-bys covering a set of query keywords well, which is useful in many applications. We develop two interesting approaches to tackle the problem. We further extend our methods to allow partial matches and matches using a keyword ontology. An extensive empirical evaluation using both real data sets and synthetic data sets is reported to verify the effectiveness of aggregate keyword search and the efficiency of our methods.     

基于关系数据库的关键词查找排序策略

基于关系数据库的关键词查找技术像使用搜索引擎一样获取数据库中相关的数据.针对RDBMS上具体书目索引数据库的关键词查找高效性问题,提出了对返回结果集的一种排序策略.以查询序列与结果元组树之间的相似值作为排序依据,参照传统信息检索系统上关键词查找结果集排序的相似值计算公式,提出数据库上查询序列与结果元组树之间的相似值公式,并分析与重新定义了相关影响因子的标准化函数表达式.通过在简单数据库上的分析验证了该改进是合理的.     

An attribute-based keyword search for m-Health networks

Journal of Computer Virology and Hacking Techniques - m-Health stands for mobile health, where mobile devices are used for collecting and distributing health-related data. As the information...     

A novel keyword search paradigm in relational databases: Object summaries

This paper introduces a novel keyword search paradigm in relational databases, where the result of a search is an Object Summary (OS). An OS summarizes all data held about a particular Data Subject (DS) in a database. More precisely, it is a tree with a tuple containing the keyword(s) as a root and neighboring tuples as children. In contrast to traditional relational keyword search, an OS comprises a more complete and therefore semantically meaningful set of information about the enquired DS.The proposed paradigm introduces the concept of Affinity in order to automatically generate OSs. More precisely, it investigates and quantifies the Affinity of relations (i.e. Affinity) and their attributes (i.e. Attribute Affinity) in order to decide which tuples and attributes to include in the OS. Experimental evaluation on the TPC-H and Northwind databases verifies the searching quality of the proposed paradigm on both large and small databases; precision, recall, f-score, CPU and space measures are presented.     

Trustworthy keyword search for compliance storage

Intense regulatory focus on secure retention of electronic records has led to a need to ensure that records are trustworthy, i.e., able to provide irrefutable proof and accurate details of past events. In this paper, we analyze the requirements for a trustworthy index to support keyword-based search queries. We argue that trustworthy index entries must be durable—the index must be updated when new documents arrive, and not periodically deleted and rebuilt. To this end, we propose a scheme for efficiently updating an inverted index, based on judicious merging of the posting lists of terms. Through extensive simulations and experiments with two real world data sets and workloads, we demonstrate that the scheme achieves online update speed while maintaining good query performance. We also present and evaluate jump indexes, a novel trustworthy and efficient index for join operations on posting lists for multi-keyword queries. Jump indexes support insert, lookup and range queries in time logarithmic in the number of indexed documents.     

Finding and ranking compact connected trees for effective keyword proximity search in XML documents

In this paper, we study the problem of keyword proximity search in XML documents. We take the disjunctive semantics among the keywords into consideration and find top-k relevant compact connected trees (CCTrees) as the answers of keyword proximity queries. We first introduce the notions of compact lowest common ancestor (CLCA) and maximal CLCA (MCLCA), and then propose compact connected trees and maximal CCTrees (MCCTrees) to efficiently and effectively answer keyword proximity queries. We give the theoretical upper bounds of the numbers of CLCAs, MCLCAs, CCTrees and MCCTrees, respectively. We devise an efficient algorithm to generate all MCCTrees, and propose a ranking mechanism to rank MCCTrees. Our extensive experimental study shows that our method achieves both high efficiency and effectiveness, and outperforms existing state-of-the-art approaches significantly.     

结合查询相关性的关键词查询排序方法

针对关系数据库关键词查询系统中的结果排序问题,提出了一种新的排序方法.该方法结合了查询相关性和结构权重,将单个元组看作是一个虚拟文档,通过对元组引入信息检索(information retrieval,JR)式评分方式,采用标准化词频和标准化逆文档频率说明元组与查询条件之间的相关性程度,对整个结果采用结构权重来反应结果的语义强度.相比于以往只考虑结构权重的排序方法,该方法能更有效的将与查询高度相关的结果排在前面.实验结果表明,结合查询相关性的排序方法可以有效的对结果进行排序.     

XML关键字检索中Dewey码存储方式的研究

Dewey码是XML关键字检索中采用的重要编码方式。在目前的研究当中,Dewey码通常以字符形式进行存储,这种方式造成Dewey码存储代价过大,并且在LCA求解过程中也必须通过字符比较才能获得Dewey码各层的数值,影响LCA求解效率。提出采用前缀共享和变长整形编码思路的PSVL存储方式,在消除字符比较操作的同时减少了Dewey码集合的存储代价。实验证明利用该存储方式对Dewey码集合进行存储,可以有效地降低其存储代价,并且减少获取Dewey码各层数值这一步骤花费的时间,间接提高了LCA的求解效率。     

云计算中加密数据的模糊关键字搜索方法

传统的可搜索加密方案仅支持精确匹配的搜索,在效率和性能上都不能适应云计算环境。用支持多种字符串相似性操作的R+树构建索引,实现了云计算中对加密数据的模糊关键字搜索;用编辑距离来量化关键字的相似度,提出了一种可以返回与关键字更接近的文件检索方法。通过字符串聚类提高了模糊关键字搜索的效率。     

加密数据库快速关键词查询技术

为保证敏感信息的数据安全,用户通常会将其加密后存储到云端数据库,这为数据库管理及后续使用增加了难度。提出一种安全查询方案,在不暴露敏感信息的情况下可获得符合查询条件的结果集。使用伪随机函数和Bloom过滤器,对敏感信息的关键词集合进行预处理,在数据库中生成相应的索引数据结构,支持不固定数量的关键词查询与高效的数据更新。查询时,客户端计算出关键词相应的陷门并将其发送给服务器,服务器使用陷门执行查询,将多关键词计算出的陷门进行串接,可将多关键词查询问题转换成单关键词查询问题,并且不提高时间复杂度。此外,有效的陷门只能由拥有密钥的用户产生,陷门不会泄露任何敏感信息,故该方案不依赖完全可信的数据库服务提供商。与现有的采用特殊双层结构的加密方式相比,提高了查询效率,解决了加密数据库处理用户查询请求时的敏感信息泄露问题,且允许用户对敏感信息采用不同的加密方式,具有很强的兼容性。使用TPC-H的数据库测试方案和测试数据进行实验,实验结果证明了算法具有较高的执行效率。     

SAIL: Structure-aware indexing for effective and progressive top-k keyword search over XML documents

Keyword search in XML documents has recently gained a lot of research attention. Given a keyword query, existing approaches first compute the lowest common ancestors (LCAs) or their variants of XML elements that contain the input keywords, and then identify the subtrees rooted at the LCAs as the answer. In this the paper we study how to use the rich structural relationships embedded in XML documents to facilitate the processing of keyword queries. We develop a novel method, called SAIL, to index such structural relationships for efficient XML keyword search. We propose the concept of minimal-cost trees to answer keyword queries and devise structure-aware indices to maintain the structural relationships for efficiently identifying the minimal-cost trees. For effectively and progressively identifying the top-k answers, we develop techniques using link-based relevance ranking and keyword-pair-based ranking. To reduce the index size, we incorporate a numbering scheme, namely schema-aware dewey code, into our structure-aware indices. Experimental results on real data sets show that our method outperforms state-of-the-art approaches significantly, in both answer quality and search efficiency.     

An effective simulation method for search strategy in unstructured P2P network

Modeling and Simulation Techniques focus on simulation modeling and analysis challenges to develop principles or gain in existing networking technologies, including simulation methodology or application in unstructured P2P network. In this paper, we focus on the simulation in the random unstructured P2P network.The correlation between topology and search algorithm is less paid attention to in unstructured P2P network, current techniques used in existing unstructured P2P system are often very inefficient because of the existence of large number of redundant messages. In this paper, we analyze the reason of engendering redundant data packet and focus on designing better search algorithm to adapt to the current network rather than to design the universal algorithm to adapt to a variety of network. Analyzing the characteristics that the flooding algorithm showing in different search stages in different network, we put forward the segment search algorithm. Furthermore, we present an effective simulation method in the random unstructured P2P network. Through simulation analysis, we find that our algorithm is better than the original flooding algorithm. In addition, we design our algorithm to be simple as a module, which can be easily applied to existing unstructured P2P systems for immediate impact.     

Proxy re-encryption with keyword search

We introduce a new cryptographic primitive, called proxy re-encryption with keyword search, which is motivated by the following scenario in email systems: Charlie sends an encrypted email, which contains some keywords, such as “urgent”, to Alice under Alice’s public key, and Alice delegates her decryption rights to Bob via her mail server. The desired situations are: (1) Bob can decrypt mails delegated from Alice by using only his private key, (2) Bob’s mail gateway, with a trapdoor from Bob, can test whether the email delegated from Alice contains some keywords, such as “urgent”, (3) Alice and Bob do not wish to give the mail server or mail gateway the access to the content of emails.The function of proxy re-encryption with keyword search (PRES) is the combination of proxy re-encryption (PRE) and public key encryption with keyword search (PEKS). However, a PRES scheme cannot be obtained by directly combining those two schemes, since the resulting scheme is no longer proven secure in our security model. In this paper, a concrete construction is proposed, which is proven secure in the random oracle model, based on the modified Decisional Bilinear Diffie-Hellman assumption.     

ABKS-SKGA: Attribute-based keyword search secure against keyword guessing attack

In order to guarantee security and privacy of sensitive data, attribute-based keyword search (ABKS) enables data owners to upload their encrypted data to cloud servers, and authorizes intended data users to retrieve it. Meanwhile, ABKS outsources heavy search work to cloud servers, which makes ABKS adaptive to mobile computing environment. However, as cloud servers can both generate keyword ciphertexts and run search algorithm, the existing most ABKS schemes are vulnerable to keyword guessing attack. In this paper, we show the fundamental cause that the existing ABKS schemes do not resist keyword guessing attack is any entity can generate keyword ciphertext. To solve the above problem, in the phase of keyword ciphertext generation, we use private key of data owner to sign keyword prior to generating keyword ciphertext. Therefore, any other entity does not forge keyword ciphertext, which can resist keyword guessing attack. We give the formal definition and security model of attributed-based keyword search secure against keyword guessing attack (ABKS-SKGA). Furthermore, we provide an ABKS-SKGA scheme. The ABKS-SKGA scheme is proved secure against chosen-plaintext attack (CPA). Performance analysis shows that the proposed scheme is practical.     

A query refinement framework for xml keyword search

Existing work of XML keyword search focus on how to find relevant and meaningful data fragments for a query, assuming each keyword is intended as part of it. However, in XML keyword search, user queries usually contain irrelevant or mismatched terms, typos etc, which may easily lead to empty or meaningless results. In this paper, we introduce the problem of content-aware XML keyword query refinement, where the search engine should judiciously decide whether a user query Q needs to be refined during the processing of Q, and find a list of promising refined query candidates which guarantee to have meaningful matching results over the XML data, without any user interaction or a second try. To achieve this goal, we build a novel content-aware XML keyword query refinement framework consisting of two core parts: (1) we build a query ranking model to evaluate the quality of a refined query RQ, which captures the morphological/semantical similarity between Q and RQ and the dependency of keywords of RQ over the XML data; (2) we integrate the exploration of RQ candidates and the generation of their matching results as a single problem, which is fulfilled within a one-time scan of the related keyword inverted lists optimally. Finally, an extensive empirical study verifies the efficiency and effectiveness of our framework.     

不确定性区域上的多维关键词搜索

随着在线地图应用的普及,基于地图的空间对象检索成为一个重要的工具而被广泛使用,技术也比较成熟。人们在地图上经常进行确定性目标点查询,例如用户提交关键词“咖啡店”,地图应用会在地图上标记所有的咖啡店,用户还可以通过进一步操作获取咖啡店的详细信息。但实际生活中存在另一种需求,例如用户想找到一个区域,在这个区域内要有“咖啡店”、“学校”和“旅店”这三类对象,称这样的查询为不确定性区域检索查询。目前对地图应用的研究无法解决不确定性区域检索的问题。而利用矩形剪枝和top-k推荐能够通过用户提交的关键字,给用户返回若干候选区域。     

Generating text search applications for databases

To develop reusable domain-based assets, the author shows how to connect a domain analysis tool's output to a program generator and then to a domain implementation's input. The generator he built using Java, XML, and Oracle tools can produce code for JSP, PL/SQL, and PSP. This domain engineering research project demonstrated that it is possible to successfully implement software product lines in industrial environments using existing tools.