Database Entities Research Articles

Mechanisms of traditional Chinese medicines in the treatment of allergic rhinitis using a network biology approach

Abstract Objective To systematically explore the effect and mechanism of traditional Chinese herbs in the treatment of allergic rhinitis. Methods Magnolia flower, xanthium fruit, astragalus root, paniculate cynanchum, scutellaria root, cicada molting, stephania root, small centipeda herb, and licorice root as traditional Chinese herbs are widely used to treat allergic rhinitis. With multiple database integration and entity grammar systems, a molecular interaction network of traditional Chinese herbs for treating allergic rhinitis was established. Bioinformatics approaches were adopted to integrate relevant data and biological information by multiple databases. Molecular interaction network of Chinese herbs for treating allergic rhinitis was constructed based on entity grammar systems model. The network was then used for elucidating the anti-allergic rhinitis mechanism of the Chinese herbs on the molecular level. The subnetworks were also extracted to explicitly display the pathway and targets where effective components acted on. Results Traditional Chinese herbs could influence various pathological aspects in allergic rhinitis including the production of pro-inflammatory substances, such as histamine and cytokine IL-4, IL-13, lowering IgE level or blocking antigen binding, altering the biological processes of IgE, modulating the balance of Th or other cells by cell proliferation and differentiation, mediating cell–cell signaling and second-messenger-mediated signaling, stabilizing the cell membrane, and affecting regulation of cellular defense response. Conclusion The research theoretically confirms the mechanism of anti-allergic rhinitis by traditional Chinese herbs, provides important fundamental research information for treatment of allergic rhinitis, and may serve as a reference for new drug development and effective ingredients of compatibility.

The Hibernate Framework Query Mechanisms Comparison

database entity object cannot be mapped directly. To solve the problem, ORM framework is proposed. The ORM framework (2) is the automatic tool which maps the structures of the classes in the application, and the tables in DMBS automatically. There are several ORM frameworks such as myBatis (3), JPA (4), (5) and Hibernate (6). Among all, Hibernate framework is used widely because of its data query facilities. There are 3 query methods for the Hibernate framework which are Hibernate Query Language (HQL), Criteria API and Query for Native SQL. These query techniques are used to reduce the complexity of SQL commands and provide the object liked query. The key of using the Hibernate in the application is not only its convenient, but also the effectiveness. There are several key factors which relate to the performance of the Hibernate application such as the amount of query records and the cache technology (7). In this study, the other factors, which may affect the query time, are investigated. The different Hibernate query mechanisms are focused in the different scenario. In Section II, the works related with the Hibernate performance analysis are discussed. The experimental design is described in Section III. The preliminary result and discussion are presented in Section IV. Finally, Section V is the conclusion and future work.

Flexible data queries

The Structured Query Language (SQL) has been established as a standard for querying relational databases. However, users face the problem how to define their requirements for data by the exact query conditions. This work examines advantages of fuzzy queries, which provide a better representation of the user requirements by expressing imprecise conditions through linguistic terms. Further, the paper discusses solving empty and overabundant answer problems, revealing similarities in database entities and applying preferences in query conditions. Finally, paper discusses practical realisations of fuzzy queries.

Interesting pattern mining in multi-relational data

Mining patterns from multi-relational data is a problem attracting increasing interest within the data mining community. Traditional data mining approaches are typically developed for single-table databases, and are not directly applicable to multi-relational data. Nevertheless, multi-relational data is a more truthful and therefore often also a more powerful representation of reality. Mining patterns of a suitably expressive syntax directly from this representation, is thus a research problem of great importance. In this paper we introduce a novel approach to mining patterns in multi-relational data. We propose a new syntax for multi-relational patterns as complete connected subsets of database entities. We show how this pattern syntax is generally applicable to multi-relational data, while it reduces to well-known tiles " Geerts et al. (Proceedings of Discovery Science, pp 278---289, 2004)" when the data is a simple binary or attribute-value table. We propose RMiner, a simple yet practically efficient divide and conquer algorithm to mine such patterns which is an instantiation of an algorithmic framework for efficiently enumerating all fixed points of a suitable closure operator "Boley et al. (Theor Comput Sci 411(3):691---700, 2010)". We show how the interestingness of patterns of the proposed syntax can conveniently be quantified using a general framework for quantifying subjective interestingness of patterns "De Bie (Data Min Knowl Discov 23(3):407---446, 2011b)". Finally, we illustrate the usefulness and the general applicability of our approach by discussing results on real-world and synthetic databases.

IDPredictor: predict database links in biomedical database.

Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data is spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments, an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge except out of the interlinked databases. A prerequisite of supporting the concept of an integrated data view is to acquire insights into cross-references among database entities. This issue is being hampered by the fact, that only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predicts and extracts cross-references from multiple life science databases and possible referenced data targets. We study the retrieval quality of our method and report on first, promising results. The method is implemented as the tool IDPredictor, which is published under the DOI 10.5447/IPK/2012/4 and is freely available using the URL: http://dx.doi.org/10.5447/IPK/2012/4.

IDPredictor: predict database links in biomedical database

Summary Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data is spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure. To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments, an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge excerpt out of the interlinked databases. A prerequisit of supporting the concept of an integrated data view is to acquire insights into cross-references among database entities. This issue is being hampered by the fact, that only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems. In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predicts and extracts cross-references from multiple life science databases and possible referenced data targets. We study the retrieval quality of our method and report on first, promising results. The method is implemented as the tool IDPredictor, which is published under the DOI 10.5447/IPK/2012/4 and is freely available using the URL: http://dx.doi.org/10.5447/IPK/2012/4.

Learning graphical models for relational data via lattice search

Many machine learning applications that involve relational databases incorporate first-order logic and probability. Relational extensions of graphical models include Parametrized Bayes Net (Poole in IJCAI, pp. 985---991, 2003), Probabilistic Relational Models (Getoor et al. in Introduction to statistical relational learning, pp. 129---173, 2007), and Markov Logic Networks (MLNs) (Domingos and Richardson in Introduction to statistical relational learning, 2007). Many of the current state-of-the-art algorithms for learning MLNs have focused on relatively small datasets with few descriptive attributes, where predicates are mostly binary and the main task is usually prediction of links between entities. This paper addresses what is in a sense a complementary problem: learning the structure of a graphical model that models the distribution of discrete descriptive attributes given the links between entities in a relational database. Descriptive attributes are usually nonbinary and can be very informative, but they increase the search space of possible candidate clauses. We present an efficient new algorithm for learning a Parametrized Bayes Net that performs a level-wise search through the table join lattice for relational dependencies. From the Bayes net we obtain an MLN structure via a standard moralization procedure for converting directed models to undirected models. Learning MLN structure by moralization is 200---1000 times faster and scores substantially higher in predictive accuracy than benchmark MLN algorithms on five relational databases.

Structure Learning for Markov Logic Networks with Many Descriptive Attributes

Many machine learning applications that involve relational databases incorporate first-order logic and probability. Markov Logic Networks (MLNs) are a prominent statistical relational model that consist of weighted first order clauses. Many of the current state-of-the-art algorithms for learning MLNs have focused on relatively small datasets with few descriptive attributes, where predicates are mostly binary and the main task is usually prediction of links between entities. This paper addresses what is in a sense a complementary problem: learning the structure of an MLN that models the distribution of discrete descriptive attributes on medium to large datasets, given the links between entities in a relational database. Descriptive attributes are usually nonbinary and can be very informative, but they increase the search space of possible candidate clauses. We present an efficient new algorithm for learning a directed relational model (parametrized Bayes net), which produces an MLN structure via a standard moralization procedure for converting directed models to undirected models. Learning MLN structure in this way is 200-1000 times faster and scores substantially higher in predictive accuracy than benchmark algorithms on three relational databases.

HOLONIC FAULT-TOLERANT CONTROL OF NETWORKED ROBOT-VISION ASSEMBLY STATIONS

The paper describes a networked fault-tolerant assembly system of job shop type containing multiple robotized stations operating under visual control. During the assembly process, the products are identified, located, qualified, and controlled by analyzing images from virtual cameras associated to stationary- and arm-mounted cameras. The holonic control architecture is formed by the following types of entities: a Global Assembly Scheduler generating Order Holons, a dual layer of Material Handling (Robot) Holons and Sensory Holons, and a System Monitoring & Database entity. These elements are embedded in a 3-layer computing & control structure, and interconnected by a 4-fold fault-tolerant communication network keeping track of assembly job execution. Implementing issues and experimental results are reported.

PDM system implementation based on UML

The paper deals with a Unified Modeling Language (UML)-based approach for implementing Product Data Management (PDM) system. Such kind of system enables the management of the entire product lifecycle and related information about its design, its manufacturing and its in-service. The interest of using an UML-based approach for modeling, specifying and implementing PDM systems is detailed. Then an implementation method is proposed in order to clarify the instantiation of UML diagrams into database entities. Regarding a business case study based on the needs of a turboprop aircraft development project, the preliminary implementation of PDM system is presented. The used UML diagrams for the modeling and integration of product, process, and resource data is detailed in order to argue the interest of an object-oriented approach in such kind of PDM implementation.

GEOCITY: a drill-hole database as a tool to assess geological hazard in Napoli urban area

The geological investigations of Napoli city, which represent the basic data source for geological hazard assessment, are characterized by the absence of well exposed and continuous outcrops, mainly due to the development of the urbanized area occurred in the last 50 years. In order to increase the set of available geological information, a database named GEOCITY has been realized on the base of 900 drill holes with stratigraphies reflecting the main drill-hole surveys performed during the last century. The true key moments in its construction were the homogenisation of different data and the identification of the geological formations. A detailed analysis of the drill-hole raw data allowed us to define the database entities, while the features of the deposits, the distribution of products and the reference drill holes were used to identify the geological formation. The analysis performed using the GEOCITY database made it possible to better assess volcanic hazard from Campi Flegrei and Somma-Vesuvio and the hazard related to alluvial phenomena that Napoli city is exposed to.

The customer is always right, but whose customer is more important?

This paper takes a social shaping of technology approach to identify and explain sources of conflict in the design or enhancement of corporate Web sites. Data from a multi‐case field study show how Web site classification schemes embedded in Web site design elements created intra‐organizational conflicts because the schemes could not equally accommodate different sub‐units' customer requirements. Interview data demonstrate Web managers' perceptions that Web classification schemes privileged certain sets of customer needs, and Web managers' actions to shape the design of classification schemes to satisfy their perceived customer needs. Data analysis identified three design elements of Web sites associated with sub‐unit conflict: classification categories, templates and tool bars, and database entities and attributes.

A family of test adequacy criteria for database-driven applications

Although a software application always executes within a particular environment, current testing methods have largely ignored these environmental factors. Many applications execute in an environment that contains a database. In this paper, we propose a family of test adequacy criteria that can be used to assess the quality of test suites for database-driven applications. Our test adequacy criteria use dataflow information that is associated with the entities in a relational database. Furthermore, we develop a unique representation of a database-driven application that facilitates the enumeration of database interaction associations. These associations can reflect an application's definition and use of database entities at multiple levels of granularity. The usage of a tool to calculate intraprocedural database interaction associations for two case study applications indicates that our adequacy criteria can be computed with an acceptable time and space overhead.

Parallel On-the-Fly Reading of an Entire Database Copy

To recover from media failures, a database is 'restored' from an earlier backup copy. A recovery log of transactions is used to roll forward from the backup version to the desired time (the current time). High availability requires that the backup copying be fast, and be in parallel with on-going update activity. It also necessitates, frequently obtaining a consistent copy of an entire database. Such concurrent generation of a database copy, interferes with system activity. It introduces blocking and delays for many update transactions. We propose an algorithm that reads current database entities without interference with update activity. The algorithm is simple to implement as compared with previous proposals. It assigns a color to each entity read by the global-read. Normal transactions commit by declaring a color for the committed updates. Subsequently, these markings are used for generation of a consistent copy of the entire database.

Database reusability in intelligent medical systems.

Reuse or reusability is not a specific, algorithmic, heuristic or only a simple set of guidelines. Database reuse means the use of an existing component--a database entity--in a new context, either elsewhere in the same system or in another system. According to different definitions, an intelligent system is a "power tool for thinking"; but on the other side it is only a kind of information system with built-in knowledge to support decisions made by human experts. Similar conclusions could be made for intelligent medical systems and introduce the database reusability in this environment with a purpose to increase the quality of an intelligent medical system. In the paper the problem of the database reusability will be presented more in detail, especially its integration in an intelligent medical system. Finally, the results of such integration and the benefits for the medicine will be discussed.

An Optimal Predicate Locking Scheduler

The paper presents a predicate locking scheduler that maximizes concurrency by locking as many of the database entities as possible without compromising the correctness of execution of the database transactions. The scheduling strategy that guarantees the maximal concurrency is first identified, then a predicate language allowing an efficient implementation of this strategy is given. The optimal predicate locking scheduler is successively presented, based on a lattice-theoretic formalization of the underlying concepts. Finally, the range of applicability of the optimal scheduling strategy is circumscribed, by showing that any significant extension to the expressive power of the predicate language accepted by the optimal scheduler causes an irreparable loss of efficiency.

Development of a DSS for Fixed-Income Securities Using OOP

I developed a simplified version of a decision support system for Health Net. Health Net needed a system to manage fixed-income portfolios worth $370 million and to analyze 36 different types of fixed-income securities and derivatives for risk and return. I integrated the required mathematical models and database system in the object-oriented paradigm by uniformly implementing mathematical models for interest rate movements and cash flows of securities, database entities, and the interface as interacting objects.

On-the-fly reading of entire databases

A common database need is to obtain a global-read, which is a consistent read of an entire database. To avoid terminating normal system activity, and thus improve availability, we propose an on-the-fly algorithm that reads database entities incrementally and allows normal transactions to proceed concurrently. The algorithm assigns each entity a color based on whether the entity has been globally read, and a shade based on how normal transactions have accessed the entity. Serializability of execution histories is ensured by requiring normal transactions to pass both a color test and a shade test before being allowed to commit. Our algorithm improves on a color-only-based scheme from the literature; the color-only scheme does not guarantee serializability. >

Relational spatial topologies for historical geographical information

Abstract Research on time and data models for Geographical Information Systems (GIS) has focused mainly in the representation of temporal geographical entities and implementation of temporal databases. Many temporal GIS database structures have been proposed but most of them just provide principles, not the recipe for the design. Owing to the manipulation of the large quantity of geographical information and the slow response time, few implementations exist. This paper presents a relational method of storing and retrieving spatial and temporal topologies. Two-level state topologies are proposed: a state topology for a set of geographical entities and a state topology for a single geographical entity. From a temporal perspective, these two-level state topologies may also be viewed as two-level time topologies: a time topology for all geographical entities in a GIS database and a time topology for a single geographical entity. Based on these state and time topologies, a detailed storage approach for histori...

Granularity issues in a knowledge-based programming environment

Marvel is a knowledge-based programming environment that assists software development teams in performing and coordinating their activities. While designing Marvel, several granularity issues were discovered that have a strong impact on the degree of intelligence that can be exhibited, as well as on the friendliness and performance of the environment. The most significant granularity issues include the refinement of software entities in the software database and decomposition of the software tools that process the entities and report their results to the human users. This paper describes the many alternative granularities and explains the choices made for Marvel.