Referential integrity quality metrics

Referential integrity quality metrics

Declarative technologies have made great strides in expressivity between SQL and SBVR. SBVR models are more expressive that SQL schemas, but not as imminently executable yet. In this paper, we complete the architecture of a system that can execute SBVR models. We do this by describing how SBVR rules can be transformed into SQL DML so that they can be automatically checked against the database using a standard SQL query. In particular, we describe a formalization of the basic structure of an SQL query which includes aggregate functions, arithmetic operations, grouping, and grouping on condition. We do this while staying within a predicate calculus semantics which can be related to the standard SBVR-LF specification and equip it with a concrete semantics for expressing business rules formally. Our approach to transforming SBVR rules into standard SQL queries is thus generic, and the resulting queries can be readily executed on a relational schema generated from the SBVR model.

Data mining is becoming increasingly important since the size of databases grows even larger and the need to explore hidden rules from the databases becomes widely recognized. Currently database systems are dominated by relational database and the ability to perform data mining using standard SQL queries will definitely ease implementation of data mining. However the performance of SQL based data mining is known to fall behind specialized implementation. In this paper we present an evaluation of parallel SQL based data mining on large scale PC cluster. The performance achieved by parallelizing SQL query for mining association rule using 4 processing nodes is even with C based program.

The growth of communication technology in the last decade has prompted researchers to develop web based learning systems for various types of applications. The entities involved in the development of such systems are learners, instructors, system designers, administrators to name a few. With varying nature of applications and entities involved, the task of evaluating these web based learning systems appears quite challenging. The present study uses factor analysis to develop an assessment model for evaluating the web based learning systems developed by the authors. The analysis is performed on a data set derived by applying a survey instrument on a set of 140 learners using these systems. These learners ranked 20 items used for evaluation in order of their perceived degree of importance. It was found that the most important dimension was learning outcome which coincides with the general outcome of any mode of learning.Keywords: Web Based Learning Systems, Web based Learning Assessment, Survey Instrument, Factor Analysis, SPSSIntroductionIn recent times the most important innovation that has changed the face of educational technology is web-based education. This has been possible due to tremendous advancement in the field of computer networks and communication technology. There are two ways in which web based learning may be implemented. The primitive form of this type of learning is Electronic Learning (E-Learning). E-Learning is a general term that is used for learning from any electronic device such as radio, television, computer and all other devices that may be invented in the near future that are electronic in nature [8]. A major disadvantage of this form of learning is lack of ubiquity and idle time utilization. The advancement in the field of mobile communications in late 90's gave birth to Mobile Learning (M-Learning). Mobility added to E-Learning is M-Learning. M-learning enables learning independently of place and time as is ubiquitous through the use of wireless networks and mobile devices [21]. The effectiveness of web based learning is enhanced when several learners attempt to learn something together. In this form of learning, learners can capitalize on one another's resources and skills. Thus Collaborative learning (C-Learning) [7],[12] techniques may be used in E-Learning and M-Learning environments to increase their efficacy.Web learning systems are multidisciplinary in nature. They are used by learners from various domains who have varied experiences of using these systems. The design goals of these systems are thus highly dependent on the nature of the application and the knowledge of the learner. Efficient design of web learning systems is also dependent on the quality of the instructors, administrators and system designers. Thus there are several factors upon which the success of a web learning system depends. The aim of this study is to develop a multi-dimension model to evaluate web learning systems. It is to be noted that this assessment model is not general in nature. It has been derived on the basis of a survey performed to assess the web learning systems developed by author [2],[3],[4].Various methods have been employed by the researchers for assessing web based learning systems. A method typically used by several researchers is to develop a set of quality metrics for this purpose. Gafani [13] has used the ISO/IEC 9126 model to propose a set of quality metrics for evaluating Mobile Learning Systems. These metrics were defined and empirically validated for mobile wireless systems. Parsons et al. [24] proposed an assessment of mobile learning quality in terms of learner experience. A conceptual frame work for a mobile learning application was constructed and quality metrics were developed for this purpose. Wang [26] developed a comprehensive model and instrument for measuring learner satisfaction with asynchronous e-learning systems. This study performed different reliability and validity tests for analyzing data from a sample of 116 respondents of a survey. …

The Rising Tide Lifts All Boats: Using the Ground and Air Medical qUality in Transport (GAMUT) Database as a Tool for Quality Improvement

The article discusses a special type of integrity in databases – ancestral multiplicity integrity (AM-integrity), a concept introduced by the authors at the conceptual–ontological level within “entity–relationship” models. In this paper, this concept is extended to the relational model for the purpose of practical application in creating databases. Related notions are examined, including the lineage of ascending ancestry, the relation of ancestral multiplicity, and the corresponding integrity constraint. Transitioning to the relational model requires consideration of primary and foreign keys, referential integrity, and table triggers. As a universal means of ensuring integrity in the relational environment, the paper proposes an approach based on database triggers. These triggers detect and block insert and update operations that would lead to violations of integrity. The need for procedural programming of triggers is noted, along with the challenges of transferring data between different database management systems due to variations in procedural programming languages. An alternative approach is suggested, based on leveraging built-in mechanisms for maintaining referential integrity, in which ancestral multiplicity integrity is ensured as part of referential integrity. The specific solution depends on the use of simple or composite, natural or surrogate keys, as well as on the technical limitations of the database implementation environment. In some cases, redundant key components may need to be introduced to track ancestor instances along ancestral lineages. Test examples of relational models are provided for different relational systems (complete implementations in MySQL and MariaDB, and partial implementations in PostgreSQL, MS SQL Server, and Oracle Database).

Least cost routing is a common practice in the telecommunications industry for international and national telephone voice minutes transactions, including buying, selling, and bilateral swapping agreements. A wholesale voice terminator purchases voice minutes from multiple suppliers and resells to its customers. Voice terminators combine and mix routes supplied by multiple providers for the purposes of increasing capacity and/or maximising profitability. However, this practice may lead to degradation of voice quality due to route inconsistancy. To protect trade secrets, voice providers usually do not disclose their upstream providers or route mix details; and therefore, it is difficult for downstream providers to detect quality issues. Some players even manipulate routes by injection tricks such as false ringtone and delayed disconnection in order to produce better quality statistics to mislead the buyers. Traditional quality metrics such as ACD and ASR are not sufficient in detecting these tricks. This paper proposes to use a suite of quality measures to detect quality issues and hidden tricks. The suite includes two metrics for benchmarking and two metrics for inequality comparison. For benchmarking, we propose to use the Q-Q plot and K-S test to test if the quality of the purchased route is the same as that of a known baseline route supplied by a reputable provider. For inequality comparison, we propose to use the Lorenz curve and Gini coefficient, mainly to detect issues that come with mixing routes.

Modeling is the first stage in a Business Process’s (BP) lifecycle. A high-quality BP model is vital to the successful implementation, execution, and monitoring stages. Different works have evaluated BP models from a quality perspective. These works either used formal verification or a set of quality metrics. This paper adopts quality metric and targets models represented in Business Process Modeling and Notation (BPMN). It proposes an approach based on fuzzy logic along with a tool system developed under eclipse framework. The preliminary experimental evaluation of the proposed system shows encouraging results.

The National Comprehensive Cancer Network (NCCN) Outcomes Database was created to assess concordance to evidence- and consensus-based guidelines and to measure adherence to quality measures on an ongoing basis. The Colorectal Cancer Database began in 2005 as a collaboration among 8 NCCN centers. Newly diagnosed colon and rectal cancer patients presenting to 1 of 8 NCCN centers between September 1, 2005, and May 21, 2008, were eligible for analysis of concordance with NCCN treatment guidelines for colorectal cancer and with a set of quality metrics jointly developed by ASCO and NCCN in 2007. Adherence rates were determined for each metric. Center-specific rates were benchmarked against mean concordance rates for all participating centers. A total of 3443 patients were evaluable. Mean concordance rates with NCCN colorectal cancer guidelines and ASCO/NCCN quality measures were generally high ( >or= 90%). However, relatively low mean concordance rates were noted for adjuvant chemotherapy treatment recommendations within 9 months of diagnosis of stage II to III rectal cancer (81%), and neoadjuvant chemoradiation in clinical T4 rectal primaries (83%). These low rates of concordance seemed to be consistent across centers. Adherence to guidelines and quality measures is generally high at institutions participating in the NCCN colorectal cancer database. Lack of documentation, patient refusal, delayed treatment initiation, and lack of consensus about whether treatment was essential were the primary reasons for nonconcordance. Measurement of concordance and the reasons for nonconcordance enable participating centers to understand and improve their care delivery systems.

Knowledge Graphs (KGs) have shown to be very important for applications such\nas personal assistants, question-answering systems, and search engines.\nTherefore, it is crucial to ensure their high quality. However, KGs inevitably\ncontain errors, duplicates, and missing values, which may hinder their adoption\nand utility in business applications, as they are not curated, e.g.,\nlow-quality KGs produce low-quality applications that are built on top of them.\nIn this vision paper, we propose a practical knowledge graph curation framework\nfor improving the quality of KGs. First, we define a set of quality metrics for\nassessing the status of KGs, Second, we describe the verification and\nvalidation of KGs as cleaning tasks, Third, we present duplicate detection and\nknowledge fusion strategies for enriching KGs. Furthermore, we give insights\nand directions toward a better architecture for curating KGs.\n

Quality-informed semi-automated event log generation for process mining

We present the results obtained through a heuristic assessment of the 2D and 3D animations in commercial off-line and on line multimedia systems. Additionally, a set of quality metrics known as CAC -Computer Animation and Communicability, has been established as well as a communicability guideline, which is a guide that can be applied by the communicability expert at the moment of designing contents for multimedia/hypermedia systems aimed at long distance education, cultural heritage, and video games, for instance. The costs to reach these results have been minimal because a laboratory or specialized equipment have not been required thanks to the involvement of the communicability heuristic assessor for multimedia systems.

Smart charging strategies for electric vehicles (EVs) require as input information such as energy requirement and dwell time. In practice, that information is often not available. However, estimations may be obtained from historical charging behavior. This paper investigates the added value of historical information for EV charging strategies based on real-world EV charging data collected in an office building parking lot with 125 chargers. Furthermore, it provides valuable insights into EV charging behavior at office building parking lots, based on a statistical analysis of the data. The added value of data availability in EV charging strategies for day-ahead planning and real-time operation of office building parking lots is assessed via a set of quality metrics that measure user satisfaction and impact on the local grid. Offline charging strategies under various degrees of available information are validated by comparing their performance with the real-time operation of the parking lot. Results show a power peak reduction of more than 50% using historical data and simple estimations of arrival times, dwell times, and energy requirement. A trade-off between power peaks and service quality (on average 4.4kWh energy not served) is observed. It was found that knowledge of individual average energy provides higher added value compared to knowing individual average dwell time in both offline planning and real-time operation of the parking lot.

An increasing number of linked datasets is published on the Web, using RDF(S)/OWL. The availability of the schema describing these datasets is crucial for their meaningful usage. A dataset may contain schema-related information, however, languages do not impose any constraint on their structure, and a gap may therefore exist between the schema and the actual instances. In this paper, we tackle the problem of evaluating this gap. We present an approach relying on both type and class profiles, as well as a set of quality metrics. We also present some experimental evaluations to illustrate the use of the proposed metrics.

Efficient data warehouse (DW) design depends on the quality of a conceptual model. In this paper, a set of quality metrics have been proposed along with the theoretical validation for quality measurement of conceptual level object-oriented multidimensional data model. The paper also describes the designer level and user level viewpoints of quality evaluation through the criteria like complexity, completeness, expressiveness and analysability. Finally, the work focuses on the empirical validation of the set of metrics and measurements. The empirical validation process shows the usefulness of the proposed metrics for the assessment of operability factor of conceptual level multidimensional data model. Significant correlations exist among the proposed quality measurements and operability factors. The proposed quality evaluation framework is verified in context of a graph semantic-based object-oriented multidimensional data model GOOMD.