Querying through a user interface

In contrast to a traditional setting where users express queries against the database schema, we assert that the semantics of data can often be understood by viewing the data in the context of the user interface (UI) of the software tool used to enter the data. That is, we believe that users will understand the data in a database by seeing the labels, drop-down menus, tool tips, or other help text that are built into the user interface. Our goal is to allow domain experts with little technical skill to understand and query data. In this paper, we present our GUi As View (Guava) framework and describe how we use forms-based UIs to generate a conceptual model that represents the information in the user interface. We then describe how we generate a query interface from the conceptual model. We characterize the resulting query language using a subset of the relational algebra. Since most application developers want to craft a physical database to meet desired performance needs, we present here a transformation channel that can be configured by instantiating one or more of our transformation operators. The channel, once configured, automatically transforms queries from our query interface into queries that address the underlying physical database and delivers query results that conform to our query interface. In this paper, we define and formalize our database transformation operators. The contributions of this paper are that first, we demonstrate the feasibility of creating a query interface based directly on the user interface and second, we introduce a general purpose database transformation channel that will likely shorten the application development process and increase the quality of the software by automatically generating software artifacts that are often made manually and are prone to errors.     

Reverse engineering database queries from examples: State-of-the-art,challenges, and research opportunities

With the popularization of data access and usage, an increasing number of users without expert knowledge of databases is required to perform data interactions. Often, these users face the challenges of writing and reformulating database queries, which consume a considerable amount of time and frequently yield unsatisfactory results. To facilitate this human–database interaction, researchers have investigated the Query By Example (QBE) paradigm in which database queries are (semi) automatically discovered from data examples given by users. This paradigm allows non-database experts to formulate queries without relying on complex query languages. In this context, this work aims to present a systematic review of the recent developments, open challenges, and research opportunities of the QBE reported in the literature. This work also describes strategies employed to leverage efficient example acquisition and query reverse engineering. The obtained results show that recent research developments have focused on enhancing the expressiveness of produced queries, minimizing user interaction, and enabling efficient query learning in the context of data retrieval, exploration, integration, and analytics. Our findings indicate that future research should concentrate efforts to provide innovative solutions to the challenges of improving controllability and transparency, considering diverse user preferences in the processes of learning personalized queries, ensuring data quality, and improving the support of additional SQL features and operators.     

A knowledge-based approach for retrieving images by content

A knowledge based approach is introduced for retrieving images by content. It supports the answering of conceptual image queries involving similar-to predicates, spatial semantic operators, and references to conceptual terms. Interested objects in the images are represented by contours segmented from images. Image content such as shapes and spatial relationships are derived from object contours according to domain specific image knowledge. A three layered model is proposed for integrating image representations, extracted image features, and image semantics. With such a model, images can be retrieved based on the features and content specified in the queries. The knowledge based query processing is based on a query relaxation technique. The image features are classified by an automatic clustering algorithm and represented by Type Abstraction Hierarchies (TAHs) for knowledge based query processing. Since the features selected for TAH generation are based on context and user profile, and the TAHs can be generated automatically by a clustering algorithm from the feature database, our proposed image retrieval approach is scalable and context sensitive. The performance of the proposed knowledge based query processing is also discussed     

Enriching the conceptual basis for query formulation through relationship semantics in databases

The rapid increase in end-user computing calls into question the suitability of existing database query languages (DBQLs). Because the typical DB end-user is not a DB specialist, it is essential that DBQLs use concepts that are as close as possible to those in the end-users’ cognitive mental model and adopt interface techniques that are suited to end-users’ abilities. Concept-based query languages are well suited for this. This realization has motivated further research in conceptual, or semantic, query approaches. However, the primary focus in this field has been on semantic query optimization, not on query formulation. In this study, we address ourselves to the problem of formulation of queries using concepts. We propose a concept-based query language, called the conceptual query language (CQL), which allows for the conceptual abstraction of database queries and exploits the rich semantics of data models to ease and facilitate query formulation.The CQL approach uses the relationship semantics of semantic data models to render transparent the technical complexities of existing DB query languages. Association semantics are also used to automatically construct query graphs and pseudo-natural language explanations of queries, and to generate SQL codes. A set theoretic formalism for conceptual queries is developed and used. This paper discusses the design of CQL, its expressive power, its implementation, and the strategies for CQL query processing. The implementation of a CQL prototype is briefly discussed in this paper. User experiments were carried out extensively and showed the advantage of CQL over alternative languages such as SQL.     

DVQ: Towards Visual Query Processing of XML Database Systems

XML has been recognized as a promising language for data exchange over the Internet. A number of query languages have been proposed for querying XML data. Most of those languages are path-expression based. One difficulty in forming path-expression based queries is that users have to know the structure of XML data against which the queries were issued. In this paper, we describe a DTD-driven visual query interface for XML database systems. With such an interface, a user can easily form path-expression based queries by clicking elements in the DTD tree displayed on the screen and supplying conditions if necessary. The interface and the query generation process are described in detail.     

Effects of query complexity and learning on novice user query performance with conceptual and logical database interfaces

Users see the database interface as the database system. A good interface enables them to formulate queries better. The semantics communicated through the interface can be classified according to abstraction levels, such as the conceptual and logical levels. With the conceptual interface, interaction is in terms of real-world concepts such as entities, objects and relationships. Current user-database interaction is mainly based on the logical interface, where interaction is in terms of abstract database concepts such as relations and joins. Many researchers argue that end users will perform better with the conceptual interface. This research tested this claim, as well as the effects of query complexity and learning, on the visual query performance of users. The experiment involved three tests: an initial test, a retention test and a relearning test. The results showed that, for complex queries, conceptual interface users achieved higher accuracy, were more confident in their answers, and spent less time on the queries. This is persistent across retention and relearning tests.     

Automatic content based image retrieval using semantic analysis

We present a new text-to-image re-ranking approach for improving the relevancy rate in searches. In particular, we focus on the fundamental semantic gap that exists between the low-level visual features of the image and high-level textual queries by dynamically maintaining a connected hierarchy in the form of a concept database. For each textual query, we take the results from popular search engines as an initial retrieval, followed by a semantic analysis to map the textual query to higher level concepts. In order to do this, we design a two-layer scoring system which can identify the relationship between the query and the concepts automatically. We then calculate the image feature vectors and compare them with the classifier for each related concept. An image is relevant only when it is related to the query both semantically and content-wise. The second feature of this work is that we loosen the requirement for query accuracy from the user, which makes it possible to perform well on users’ queries containing less relevant information. Thirdly, the concept database can be dynamically maintained to satisfy the variations in user queries, which eliminates the need for human labor in building a sophisticated initial concept database. We designed our experiment using complex queries (based on five scenarios) to demonstrate how our retrieval results are a significant improvement over those obtained from current state-of-the-art image search engines.     

A transaction log analysis of a digital library

As experimental digital library testbeds gain wider acceptance and develop significant user bases, it becomes important to investigate the ways in which users interact with the systems in practice. Transaction logs are one source of usage information, and the information on user behavior can be culled from them both automatically (through calculation of summary statistics) and manually (by examining query strings for semantic clues on search motivations and searching strategy). We have conducted a transaction log analysis on user activity in the Computer Science Technical Reports Collection of the New Zealand Digital Library, and report insights gained and identify resulting search interface design issues. Specifically, we present the user demographics available with our library, discuss the use of operators and search options in queries, and examine patterns in query construction and refinement. We also describe common mistakes in searching, and examine the distribution of query terms appearing in the logs. Received: 17 December 1998/Revised: 25 May 1999     

Similarity queries: their conceptual evaluation, transformations, and processing

Many application scenarios can significantly benefit from the identification and processing of similarities in the data. Even though some work has been done to extend the semantics of some operators, for example join and selection, to be aware of data similarities, there has not been much study on the role and implementation of similarity-aware operations as first-class database operators. Furthermore, very little work has addressed the problem of evaluating and optimizing queries that combine several similarity operations. The focus of this paper is the study of similarity queries that contain one or multiple first-class similarity database operators such as Similarity Selection, Similarity Join, and Similarity Group-by. Particularly, we analyze the implementation techniques of several similarity operators, introduce a consistent and comprehensive conceptual evaluation model for similarity queries, and present a rich set of transformation rules to extend cost-based query optimization to the case of similarity queries.     

Extending a Natural Language Interface with Geospatial Queries

In this installation of architectural perspectives, we describe an extension of a menu-based natural language interface (MBNLI) to support geospatial queries. Our extension makes it easier for application analysts and even inexperienced users to phrase complex queries without knowing the relational database query language SQL, database schemas (table structures), spatial operators, or spatial indexes.     

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.     

Query-by-Gaming: Interactive spatio-temporal querying and retrieval using gaming controller

Spatio-temporal querying and retrieval is a challenging task due to the lack of simple user interfaces for building queries despite the availability of powerful indexing structures and querying languages. In this paper, we propose Query-by-Gaming scheme for spatio-temporal querying that can benefit from gaming controller for building queries. By using Query-by-Gaming, we introduce our spatio-temporal querying and retrieval system named as GStar to interactively build subsequent spatio-temporal queries to determine if a state is directly reachable from current state and eventual spatio-temporal queries to know whether a spatial state is reachable from a current state. Queries are built using features of gaming controller by displaying the original video frames rather than on a graphical interface using a mouse or a keyboard. GStar has three main components: building the query, searching and retrieval of clips, and displaying query results. The queries are applied to an indexing structure called semantic sequence state graph (S³G) and results of the query are displayed dynamically to provide timely feedback to the user. Experimental results and user interface are provided for a tennis video database. Users define desired game state (player and ball position) using an interactive interface at multiple points in time and GStar automatically retrieves all rallies that contain both states. Finally, the user interface evaluation comparing gamepad-based interface and mouse interface for spatio-temporal querying has been studied.     

数据库关键字查询技术研究

随着互联网技术与数据库技术的不断发展和相互结合,越来越多的用户需要访问在线数据库来获取各种信息,在该过程中,用户要对数据库中的数据进行查询。因此,用户就必需要掌握一定的结构化查询语言SQL,而且还要对数据库模式有一个认识和了解。但事实上,多数用户并不会使用结构化查询语言,也不了解数据库模式。所以,便产生了一个很自然的用户需求——数据库支持基于关键字的查询,文章主要就是对数据库支持基于关键字的查询进行简要的分析和探讨。     

元数据驱动的个性化查询工具设计与实现

传统查询定制工具只关注动态组合SQL语句,并没有关注与业务相关的实体,如用户、专业等。用户无法定制个性化的查询,企业不能对数据按专业、查询对象和用户等组织多维度、多专业的数据查询。为解决上述问题,提出了一个元数据驱动的个性化查询定制框架,用元数据描述用户需求和企业环境。用户通过个性定制工具,形成用户需求的元数据描述,查询引擎通过元数据读取用户需求,然后查询专业数据库并形成个性化界面。既有通用查询的通用数据接口,又有友好、个性化的用户接口,在油田企业信息集成中得到应用,并取得良好应用效果。     

Extracting a statistical data matrix from electronic patient records

This paper describes the processing and transformation of medical data from a clinical database to a statistical data matrix. Precise extraction and linking tools must be available for the desired data to be processed for statistical purposes. We show that flexible mechanisms are required for the different types of users, such as physicians and statisticians. In our retrieval tools we use logical queries based on operands and operators. The paper describes the method and appliance of the operators with which the desired matrix is created through a process of selection and linking. Examples with a Kaplan-Meier function and time-dependent covariables demonstrate how our model is useful for different user groups.     

Précis: from unstructured keywords as queries to structured databases as answers

Précis queries represent a novel way of accessing data, which combines ideas and techniques from the fields of databases and information retrieval. They are free-form, keyword-based, queries on top of relational databases that generate entire multi-relation databases, which are logical subsets of the original ones. A logical subset contains not only items directly related to the given query keywords but also items implicitly related to them in various ways, with the purpose of providing to the user much greater insight into the original data. In this paper, we lay the foundations for the concept of logical database subsets that are generated from précis queries under a generalized perspective that removes several restrictions of previous work. In particular, we extend the semantics of précis queries considering that they may contain multiple terms combined through the AND, OR, and NOT operators. On the basis of these extended semantics, we define the concept of a logical database subset, we identify the one that is most relevant to a given query, and we provide algorithms for its generation. Finally, we present an extensive set of experimental results that demonstrate the efficiency and benefits of our approach.     

QBD*: a graphical query language with recursion

A system to query databases using diagrams as a standard user interface is proposed. The system, called Query by Diagram* (QBD*), makes use of a conceptual data model, a query language on this model, and a graphical user interface. The conceptual model is the entity-relationship model. The query language, whose expressive power allows recursive queries, supports visual interaction. The main characteristics of the interface are ease of use and the availability of a rich set of primitives for schema selection and query formulation. The expressive power of QBD* and G⁺, which are the only languages allowing recursive queries to be expressed graphically are compared     

Distribution-Driven Visualization of Volume Data

Feature detection and display are the essential goals of the visualization process. Most visualization software achieves these goals by mapping properties of sampled intensity values and their derivatives to color and opacity. In this work, we propose to explicitly study the local frequency distribution of intensity values in broader neighborhoods centered around each voxel. We have found frequency distributions to contain meaningful and quantitative information that is relevant for many kinds of feature queries. Our approach allows users to enter predicate-based hypotheses about relational patterns in local distributions and render visualizations that show how neighborhoods match the predicates. Distributions are a familiar concept to nonexpert users, and we have built a simple graphical user interface for forming and testing queries interactively. The query framework readily applies to arbitrary spatial data sets and supports queries on time variant and multifield data. Users can directly query for classes of features previously inaccessible in general feature detection tools. Using several well-known data sets, we show new quantitative features that enhance our understanding of familiar visualization results.     

Towards practical private processing of database queries over public data

Privacy is a major concern when users query public online data services. The privacy of millions of people has been jeopardized in numerous user data leakage incidents in many popular online applications. To address the critical problem of personal data leakage through queries, we enable private querying on public data services so that the contents of user queries and any user data are hidden and therefore not revealed to the online service providers. We propose two protocols for private processing of database queries, namely BHE and HHE. The two protocols provide strong query privacy by using Paillier’s homomorphic encryption, and support common database queries such as range and join queries by relying on the bucketization of public data. In contrast to traditional Private Information Retrieval proposals, BHE and HHE only incur one round of client server communication for processing a single query. BHE is a basic private query processing protocol that provides complete query privacy but still incurs expensive computation and communication costs. Built upon BHE, HHE is a hybrid protocol that applies ciphertext computation and communication on a subset of the data, such that this subset not only covers the actual requested data but also resembles some frequent query patterns of common users, thus achieving practical query performance while ensuring adequate privacy levels. By using frequent query patterns and data specific privacy protection, HHE is not vulnerable to the traditional attacks on k-Anonymity that exploit data similarity and skewness. Moreover, HHE consistently protects user query privacy for a sequence of queries in a single query session.