Underlying Database Schema Research Articles

Checking inference-proofness of attribute-disjoint and duplicate-preserving fragmentations

The transmission of own and partly confidential data to another agent comes along with the risk of enabling the receiver to infer information he is not entitled to learn. We consider a specific countermeasure against unwanted inferences about associations between data values whose combination of attributes are declared to be sensitive. This countermeasure fragments a relation instance into attribute-disjoint and duplicate-preserving projections such that no sensitive attribute combination is contained in any projection. Unfortunately, the intended goal of inference-proofness will not always be accomplished. Inferences might be based on combinatorial reasoning, since duplicate-preservation implies that the frequencies of value associations in visible fragments equals those in the original relation instance. In addition, the receiver might exploit entailment reasoning about functional dependencies, numerical dependencies and tuple-generating dependencies, as presumably known from the underlying database schema. We investigate possible interferences of combinatorial reasoning and entailment reasoning and identify basic conditions for a fragmentation to violate inference-proofness. Moreover, we outline a comprehensive method to effectively check the inference-proofness of a given fragmentation and we experimentally evaluate the computational efficiency of a partial prototype implementation.

Summarizing database schema based on graph partition

As the underlying database schemas become larger and more complex, it is difficult for casual users to understand the schemas and contents of databases. Therefore, it has become an essential task to summarize the database schemas. However, most prior approaches pay little attention to the topological characteristics between tables, ignore the effect of the user feedback, and fail to accurately predict the number of clusters in the output. This seriously limits their accuracy of schema summarization. To deal with the problems, we propose a new schema summarization method based on a graph partition mechanism. First, we introduce a novel strategy to construct a similarity matrix between tables, which is based on the topology compactness, content similarity and query logs. Then we provide a calculation formula for table importance and a detection scheme of the most important nodes in local areas. Both are used for selecting the initial cluster centers and predicting the number of clusters in the graph partition mechanism. Finally, we evaluate the proposed method over the database TPC-E, and results demonstrate that it achieves high performance in summarizing accuracy.

SQLizer: query synthesis from natural language

This paper presents a new technique for automatically synthesizing SQL queries from natural language (NL). At the core of our technique is a new NL-based program synthesis methodology that combines semantic parsing techniques from the NLP community with type-directed program synthesis and automated program repair. Starting with a program sketch obtained using standard parsing techniques, our approach involves an iterative refinement loop that alternates between probabilistic type inhabitation and automated sketch repair. We use the proposed idea to build an end-to-end system called SQLIZER that can synthesize SQL queries from natural language. Our method is fully automated, works for any database without requiring additional customization, and does not require users to know the underlying database schema. We evaluate our approach on over 450 natural language queries concerning three different databases, namely MAS, IMDB, and YELP. Our experiments show that the desired query is ranked within the top 5 candidates in close to 90% of the cases and that SQLIZER outperforms NALIR, a state-of-the-art tool that won a best paper award at VLDB'14.

Scalable top-k keyword search in relational databases

Keyword search in relational databases has been widely studied in recent years because it does not require users neither to master a certain structured query language nor to know the complex underlying database schemas. There would be a huge number of valid results for a keyword query in a large database. However, only the top 10 or 20 most relevant matches for the keyword query—according to some definition of “Relevance”—are generally of interest. In this paper, we propose an efficient method which can efficiently compute the top-k results for keyword queries in a pipelined pattern, by incorporating the ranking mechanisms into the query processing method. Four optimization methods based on bounding the relevance scores of potential results, reusing and sharing the intermediate result are presented to improve the efficiency of the proposed algorithms. Compared to the existing top-k keyword search systems, the proposed methods can significantly reduce the number of computed query results with low relevance scores and the times for accessing databases, which result in the high efficiency in computing top-k keyword query results in relational databases. Extensive experiments on two real data sets are conducted to evaluate the effectiveness and efficiency of the proposed approach.

Computer-Aided Clinical Trial Recruitment Based on Domain-Specific Language Translation: A Case Study of Retinopathy of Prematurity.

Reusing the data from healthcare information systems can effectively facilitate clinical trials (CTs). How to select candidate patients eligible for CT recruitment criteria is a central task. Related work either depends on DBA (database administrator) to convert the recruitment criteria to native SQL queries or involves the data mapping between a standard ontology/information model and individual data source schema. This paper proposes an alternative computer-aided CT recruitment paradigm, based on syntax translation between different DSLs (domain-specific languages). In this paradigm, the CT recruitment criteria are first formally represented as production rules. The referenced rule variables are all from the underlying database schema. Then the production rule is translated to an intermediate query-oriented DSL (e.g., LINQ). Finally, the intermediate DSL is directly mapped to native database queries (e.g., SQL) automated by ORM (object-relational mapping).

Data perspectives of workflow schema evolution

Purpose – Most literature on workflow (WF) adaptation considered the control flow correctness like absence of dead lock, live-lock, etc. during adaptation. The data aspect of WF adaptation like data flow, database schema changes and their correctness are less studied. When the WF schema is modified, their data flow and the database schema changes. The existing approaches used for adapting these data changes in the underlying database schema are time consuming and/or affect the old data persistence. The purpose of this paper is to concern the dynamic adaptation of the WF schema and implementing its data changes in the existing database schema. Design/methodology/approach – A conceptual framework developed to adapt on-the-fly, the concomitant data changes during WF adaptation. The framework consists a set of data schema compliance criteria (DSC) which identify the data changes that can be directly accommodated in the existing database schema. Data adaptation algorithm (DAA) is developed to handle the data changes that does not conform to the DSC in the existing database schema. Findings – In this approach the existing database schema is dynamically evolved without re-creating it, after WF schema adaptation. Therefore the WF schema changes can be implemented on-the-fly without stopping the running system. It also ensures the persistence of old data residing in the existing database. Originality/value – A novel approach developed to adapt the data changes in the existing database schema, without requiring recreation or migration the data. This automated consistency checking of data attribute changes in the database schema and implement them dynamically.

A graph-theoretic approach to optimize keyword queries in relational databases

Keyword search can provide users an easy method to query large and complex databases without any knowledge of structured query languages or underlying database schema. Most of the existing studies have focused on generating candidate structured queries relevant to keywords. Due to the large size of generated queries, the execution costs may be prohibitive. However, existing studies lack the idea of a generalized method to optimize the plan of the large set of generated queries. In this paper, we introduce a graph-theoretic optimization approach. We propose a general graph model, Weighted Operator Graph, to address the costs of keyword query evaluation plans. The proposed model is flexible to integrate all of the cost-based plans in a uniform way. We define a Keyword Query Optimization Problem based on a theoretical cost model as a graph-theoretic problem and show it to be a NP-hard problem. We propose a greedy heuristic Maximum Propagation that reduces the size of the intermediate result as early as possible. The proposed algorithm allows us to achieve efficiency in terms of query evaluation costs. The experimental studies on both synthetic and real data set results show that our work outperforms the existing work.

An interactive tool for UML class model evolution in database applications

In the context of model-driven development of database applications with UML, the (usually relational) database schema is obtained automatically from the application's structural (class) UML model. Changes in requirements often lead to modifications of the application's structural model. Such changes, in turn, have to be propagated to the underlying database schema. Very often, especially when the system is in production with a large volume of users' live data, the data is considered to be valuable enough to be preserved through these changes. This paper describes an approach to cope with the problem of model evolution with the ultimate requirement to preserve the data stored in the database. The algorithm interactively determines differences between structural UML models before and after the changes and resolves those differences into transformations in the relational database domain.

Scalable continual top-k keyword search in relational databases

Keyword search in relational databases has been widely studied in recent years because it requires users neither to master a certain structured query language nor to know the complex underlying database schemas. Most of the existing methods focus on answering snapshot keyword queries in static databases. In reality, however, databases are updated frequently, and users may have long-term interests in specific topics. To deal with such a situation, it is necessary to build an effective and efficient facility in a database system to support continual keyword queries.In this paper, we propose an efficient method for answering continual top-k keyword queries over relational databases. The proposed method is built on an existing scheme of keyword search on relational data streams, but incorporates the ranking mechanisms into the query processing methods and makes two optimizations to support top-k keyword search in relational databases. Compared to the existing methods, our method is more efficient both in computing the snapshot top-k results and in maintaining the top-k results when the database is continually updated. Experimental results validate the effectiveness and efficiency of the proposed method.

Ontology-driven relational query formulation using the semantic and assertional capabilities of OWL-DL

This work investigates the extent to which domain knowledge, expressed in a domain ontology, can assist end-users in formulating relational queries that can be executed over a complex relational database. In this regard, an ontology-driven query formulation architectural framework has been devised, namely OntoQF, that implements a two-phased approach – the pre-processing and translation phases. In the pre-processing phase, a new database-to-ontology transformation approach has been synthesised where domain ontology is populated and enriched with problem domain concepts and semantic relationships specified using OWL-DL. Once domain ontology has been formulated, end-users can write sophisticated ontology-based queries that are then translated, in the translation phase, into the corresponding relational query statements. In order to validate the correctness of translating single or multiple OWL-DL constructs into their corresponding relational ones, a set of test cases have been derived from the medical domain. Our results demonstrated that the OntoQF framework enriches domain ontology and its associated algorithms drive the process of relational query formulation without the need to both replicate transactional data into the associated domain ontology and have knowledge of the underlying database schema.

A natural language interface plug-in for cooperative query answering in biological databases

BackgroundOne of the many unique features of biological databases is that the mere existence of a ground data item is not always a precondition for a query response. It may be argued that from a biologist's standpoint, queries are not always best posed using a structured language. By this we mean that approximate and flexible responses to natural language like queries are well suited for this domain. This is partly due to biologists' tendency to seek simpler interfaces and partly due to the fact that questions in biology involve high level concepts that are open to interpretations computed using sophisticated tools. In such highly interpretive environments, rigidly structured databases do not always perform well. In this paper, our goal is to propose a semantic correspondence plug-in to aid natural language query processing over arbitrary biological database schema with an aim to providing cooperative responses to queries tailored to users' interpretations.ResultsNatural language interfaces for databases are generally effective when they are tuned to the underlying database schema and its semantics. Therefore, changes in database schema become impossible to support, or a substantial reorganization cost must be absorbed to reflect any change. We leverage developments in natural language parsing, rule languages and ontologies, and data integration technologies to assemble a prototype query processor that is able to transform a natural language query into a semantically equivalent structured query over the database. We allow knowledge rules and their frequent modifications as part of the underlying database schema. The approach we adopt in our plug-in overcomes some of the serious limitations of many contemporary natural language interfaces, including support for schema modifications and independence from underlying database schema.ConclusionsThe plug-in introduced in this paper is generic and facilitates connecting user selected natural language interfaces to arbitrary databases using a semantic description of the intended application. We demonstrate the feasibility of our approach with a practical example.

Neisseria Base: a comparative genomics database for Neisseria meningitidis

Neisseria meningitidis is an important pathogen, causing life-threatening diseases including meningitis, septicemia and in some cases pneumonia. Genomic studies hold great promise for N. meningitidis research, but substantial database resources are needed to deal with the wealth of information that comes with completely sequenced and annotated genomes. To address this need, we developed Neisseria Base (NBase), a comparative genomics database and genome browser that houses and displays publicly available N. meningitidis genomes. In addition to existing N. meningitidis genome sequences, we sequenced and annotated 19 new genomes using 454 pyrosequencing and the CG-Pipeline genome analysis tool. In total, NBase hosts 27 complete N. meningitidis genome sequences along with their associated annotations. The NBase platform is designed to be scalable, via the underlying database schema and modular code architecture, such that it can readily incorporate new genomes and their associated annotations. The front page of NBase provides user access to these genomes through searching, browsing and downloading. NBase search utility includes BLAST-based sequence similarity searches along with a variety of semantic search options. All genomes can be browsed using a modified version of the GBrowse platform, and a plethora of information on each gene can be viewed using a customized details page. NBase also has a whole-genome comparison tool that yields single-nucleotide polymorphism differences between two user-defined groups of genomes. Using the virulent ST-11 lineage as an example, we demonstrate how this comparative genomics utility can be used to identify novel genomic markers for molecular profiling of N. meningitidis.Database URL: http://nbase.biology.gatech.edu

Artemis

A central feature of relational database management systems is the ability to define multiple different views over an underlying database schema. Views provide a method of defining access control to the underlying database, since a view exposes a part of the database and hides the rest. Views also provide logical data independence to application programs that access the database. For most cases, the process of specifying the desired views in SQL is typically tedious and error-prone. While numerous tools exist to support developers in debugging program code, we are not aware of any tool that supports developers in verifying the correctness of their views defined in SQL.

Motivated Proteins: A web application for studying small three-dimensional protein motifs

BackgroundSmall loop-shaped motifs are common constituents of the three-dimensional structure of proteins. Typically they comprise between three and seven amino acid residues, and are defined by a combination of dihedral angles and hydrogen bonding partners. The most abundant of these are αβ-motifs, asx-motifs, asx-turns, β-bulges, β-bulge loops, β-turns, nests, niches, Schellmann loops, ST-motifs, ST-staples and ST-turns.We have constructed a database of such motifs from a range of high-quality protein structures and built a web application as a visual interface to this.DescriptionThe web application, Motivated Proteins, provides access to these 12 motifs (with 48 sub-categories) in a database of over 400 representative proteins. Queries can be made for specific categories or sub-categories of motif, motifs in the vicinity of ligands, motifs which include part of an enzyme active site, overlapping motifs, or motifs which include a particular amino acid sequence. Individual proteins can be specified, or, where appropriate, motifs for all proteins listed. The results of queries are presented in textual form as an (X)HTML table, and may be saved as parsable plain text or XML. Motifs can be viewed and manipulated either individually or in the context of the protein in the Jmol applet structural viewer. Cartoons of the motifs imposed on a linear representation of protein secondary structure are also provided. Summary information for the motifs is available, as are histograms of amino acid distribution, and graphs of dihedral angles at individual positions in the motifs.ConclusionMotivated Proteins is a publicly and freely accessible web application that enables protein scientists to study small three-dimensional motifs without requiring knowledge of either Structured Query Language or the underlying database schema.

Advanced genomic data mining.

Advanced genomic data mining.

SEEKER: Keyword-Based Information Retrieval over Relational Databases

Traditionally, SQL is the main interface to access data from relational databases. However, it is difficult for inexperienced end users to learn the complicated syntax of SQL. Enabling keyword-based information retrieval over relational databases will allow users to acquire information from databases without any knowledge of SQL and the underlying database schema, just like the way of common search engines. This paper describes the design and implementation of SEEKER, a system supporting keyword-based information retrieval over relational databases. While there have been some existing systems that support searching text attributes in relational databases, SEEKER can also search database metadata and numeric attributes. Moreover, SEEKER employs an improved ranking function and supports Top-k queries. Experimental results show that SEEKER can achieve good retrieval performance.

WWW interfaces for runtime relational database applications

The Alcator C-Mod group has been using an electronic lab notebook based on a relational database since the experiment started in 1991. The software, called logbook, provides a shared lab notebook abstraction for all of the participating scientists. It currently contains more than 37 000 text entries. This application and its underlying database schema are also being used at DIIID and NSTX in the US, and installations at TCV at EPFL and H1 at ANU are in progress. A new WEB based user interface is being developed, modeled on the ENTRY_DISPLAY interface, which was written in IDL™. It provides complete read/write access to the entries table to any authorized user via a WEB browser. This interface places much lower demands on network and computing resources than the IDL/X-Windows based version.

GeneClinics: a hybrid text/data electronic publishing model using XML applied to clinical genetic testing.

GeneClinics is an online genetic information resource consisting of descriptions of specific inherited disorders ("disease profiles") as well as information on the role of genetic testing in the diagnosis, management, and genetic counseling of patients with these inherited conditions. GeneClinics is intended to promote the use of genetic services in medical care and personal decision making by providing health care practitioners and patients with information on genetic testing for specific inherited disorders. GeneClinics is implemented as an object-oriented database containing a combination of data and semistructured text that is rendered as HTML for publishing a given "disease profile" on the Web. Content is acquired from authors via templates, converted to an XML document reflecting the underlying database schema (with tagging of embedded data), and then loaded into the database and subjected to peer review. The initial implementation of a production system and the first phase of population of the GeneClinics database content are complete. Further expansion of the content to cover more disease, significant scaling up of rate of content creation, and evaluation redesign are under way. The ultimate goal is to have an entry in GeneClinics for each entry in the GeneTests directory of medical genetics laboratories-that is, for each disease for which clinical genetic testing is available.

Integrating Database and Dialogue Design

The user interface of data-intensive information systems may be described as a collection of semi-independent dialogues with underlying procedures accessing databases. It will be shown how to conceptualize dialogues via dialogue objects. These are organized in an object oriented fashion by dialogue classes modelled as extended database views. The description is based on an object oriented model which enables a smooth integration of dialogues with the underlying database schema. The resulting integrated model can serve as a unifying conceptual umbrella encompassing both databases and user-interfaces. The only remaining task for the presentation layer consists of suitable ergonomic presentations of dialogue objects on the screen by means of a general user interface management system. This approach may help in the development of information systems that aim at the support of highly skilled users. The design of such systems has to cope with the specific work conditions as being self-organized, equipped with certain duties and responsibilities and dependent on some environmental knowledge. Such systems are supposed to leave fundamental decisions to their users, e.g. to continue or interrupt a certain piece of work or to switch from one application to another in order to achieve a seamless embedding in organisational processes and work flow.

Lightweight databases

Current World-Wide Web technologies concentrate on presenting documents to human readers. Although HTML identifies structures within a document, it does not allow the semantic content of document sections to be specified explicitly. We investigate a small extension to HTML which allows parts of a document to be mapped onto an underlying database schema. This allows automatic identification and extraction of key information from a web using standard database techniques. Such “lightweight” databases may span servers, with searches being performed at client- or server-side. We have applied this approach to generating “flattened” versions of hypertext documents suitable for printing.