278,523 research outputs found
UML Class Diagram or Entity Relationship Diagram : An Object Relational Impedance Mismatch
It is now nearly 30 years since Peter Chenâs watershed paper âThe Entity-Relationship Model âtowards a Unified View of Dataâ. [1] The entity relationship model and variations and extensions to ithave been taught in colleges and universities for many years. In his original paper Peter Chen looked at converting his new ER model to the then existing data structure diagrams for the Network model. In recent years there has been a tendency to use a Unified Modelling Language (UML) class diagram forconceptual modeling for relational databases, and several popular course text books use UMLnotation to some degree [2] [3]. However Object and Relational technology are based on different paradigms. In the paper we argue that the UML class diagram is more of a logical model (implementation specific). ER Diagrams on theother hand, are at a conceptual level of database design dealing with the main items and their relationships and not with implementation specific detail. UML focuses on OOAD (Object Oriented Analysis and Design) and is navigational and program dependent whereas the relational model is set based and exhibits data independence. The ER model provides a well-established set of mapping rules for mapping to a relational model. In this paper we look specifically at the areas which can cause problems for the novice databasedesigner due to this conceptual mismatch of two different paradigms. Firstly, transferring the mapping of a weak entity from an Entity Relationship model to UML and secondly the representation of structural constraints between objects. We look at the mixture of notations which students mistakenly use when modeling. This is often the result of different notations being used on different courses throughout their degree. Several of the popular text books at the moment use either a variation of ER,UML, or both for teaching database modeling. At the moment if a student picks up a text book they could be faced with either; one of the many ER variations, UML, UML and a variation of ER both covered separately, or UML and ER merged together. We regard this problem as a conceptual impedance mismatch. This problem is documented in [21] who have produced a catalogue of impedance mismatch problems between object-relational and relational paradigms. We regard the problems of using UML class diagrams for relational database design as a conceptual impedance mismatch as the Entity Relationship model does not have the structures in the model to deal with Object Oriented concepts Keywords: EERD, UML Class Diagram, Relational Database Design, Structural Constraints, relational and object database impedance mismatch. The ER model was originally put forward by Chen [1] and subsequently extensions have been added to add further semantics to the original model; mainly the concepts of specialisation, generalisation and aggregation. In this paper we refer to an Entity-Relationship model (ER) as the basic model and an extended or enhanced entity-relationship model (EER) as a model which includes the extra concepts. The ER and EER models are also often used to aid communication between the designer and the user at the requirements analysis stage. In this paper when we use the term âconceptual modelâ we mean a model that is not implementation specific.ISBN: 978-84-616-3847-5 3594Peer reviewe
Contested Transparency: Digital Monitoring Technologies and Worker Voice
Advances in artificial intelligence and data analytics have notably expanded employersâ
monitoring and surveillance capabilities, facilitating the accurate observability of work
effort. There is an ongoing debate among academics and policymakers about the
productivity and broader welfare implications of digital monitoring (DM) technologies. In
this context, many countries confer information, consultation and codetermination rights
to employee representation (ER) bodies on matters related to the workplace governance
of these technologies. Using a cross-sectional sample of more than 21000 European
establishments, we document a positive association between ER and the utilization of DM
technologies. We also find a positive effect of ER on DM utilization in the context of a localrandomization regression discontinuity analysis that exploits size-contingent policy rules
governing the operation of ER bodies in Europe. Finally, in an exploratory analysis, we find
a positive association between DM and process innovations, particularly in establishments
where ER bodies are present and a large fraction of workers perform jobs that require
finding solutions to unfamiliar problems. We interpret these findings through the lens of
a labor discipline model in which the presence of ER bodies affect employerâs decision to
invest in DM technologies
ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction
A plethora of approaches have been proposed for joint entity-relation (ER)
extraction. Most of these methods largely depend on a large amount of manually
annotated training data. However, manual data annotation is time consuming,
labor intensive, and error prone. Human beings learn using both data (through
induction) and knowledge (through deduction). Answer Set Programming (ASP) has
been a widely utilized approach for knowledge representation and reasoning that
is elaboration tolerant and adept at reasoning with incomplete information.
This paper proposes a new approach, ASP-enhanced Entity-Relation extraction
(ASPER), to jointly recognize entities and relations by learning from both data
and domain knowledge. In particular, ASPER takes advantage of the factual
knowledge (represented as facts in ASP) and derived knowledge (represented as
rules in ASP) in the learning process of neural network models. We have
conducted experiments on two real datasets and compare our method with three
baselines. The results show that our ASPER model consistently outperforms the
baselines
A Rule-based Methodology and Feature-based Methodology for Effect Relation Extraction in Chinese Unstructured Text
The Chinese language differs significantly from English, both in lexical representation and grammatical structure. These differences lead to problems in the Chinese NLP, such as word segmentation and flexible syntactic structure. Many conventional methods and approaches in Natural Language Processing (NLP) based on English text are shown to be ineffective when attending to these language specific problems in late-started Chinese NLP. Relation Extraction is an area under NLP, looking to identify semantic relationships between entities in the text. The term âEffect Relationâ is introduced in this research to refer to a specific content type of relationship between two entities, where one entity has a certain âeffectâ on the other entity. In this research project, a case study on Chinese text from Traditional Chinese Medicine (TCM) journal publications is built, to closely examine the forms of Effect Relation in this text domain. This case study targets the effect of a prescription or herb, in treatment of a disease, symptom or body part. A rule-based methodology is introduced in this thesis. It utilises predetermined rules and templates, derived from the characteristics and pattern observed in the dataset. This methodology achieves the F-score of 0.85 in its Named Entity Recognition (NER) module; 0.79 in its Semantic Relationship Extraction (SRE) module; and the overall performance of 0.46. A second methodology taking a feature-based approach is also introduced in this thesis. It views the RE task as a classification problem and utilises mathematical classification model and features consisting of contextual information and rules. It achieves the F-scores of: 0.73 (NER), 0.88 (SRE) and overall performance of 0.41. The role of functional words in the contemporary Chinese language and in relation to the ERs in this research is explored. Functional words have been found to be effective in detecting the complex structure ER entities as rules in the rule-based methodology
Compact Representation of Photosynthesis Dynamics by Rule-based Models (Full Version)
Traditional mathematical models of photosynthesis are based on mass action
kinetics of light reactions. This approach requires the modeller to enumerate
all the possible state combinations of the modelled chemical species. This
leads to combinatorial explosion in the number of reactions although the
structure of the model could be expressed more compactly. We explore the use of
rule-based modelling, in particular, a simplified variant of Kappa, to
compactly capture and automatically reduce existing mathematical models of
photosynthesis. Finally, the reduction procedure is implemented in BioNetGen
language and demonstrated on several ODE models of photosynthesis processes.
This is an extended version of the paper published in proceedings of 5th
International Workshop on Static Analysis and Systems Biology (SASB) 2014.Comment: SASB 2014 full pape
Evolutionary Algorithms for Reinforcement Learning
There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications
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