Robust relational layout synthesis from examples for Android
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We present a novel approach for synthesizing robust relational layouts from examples. Given an application design consisting of a set of views and their location on the screen, we synthesize a relational layout that when rendered, places the components at that same location. We present an end-to-end system, called InferUI, that addresses the above challenge in the context of Android. The system is based on the following technical contributions: (i) a formalization of the latest and most efficient ConstraintLayout class, capturing a rich set of relational constraints, (ii) a set of robustness properties designed to prevent common layout generalization errors, (iii) a synthesis algorithm that produces relational layouts that generalize across multiple screen sizes and resolutions, and (iv) a probabilistic model of constraints that guides the synthesizer towards layouts preferred by developers. Our evaluation shows that InferUI is practically effective: it successfully synthesizes real world complex layouts obtained from top 500 GitHub and top 500 Google Play Store applications, succeeds in 100% of the cases when synthesizing layouts for a single device, and correctly generalizes 92% of the views across multiple devices, all without requiring additional specifications.Keywords:
Robustness
Has built an real time relational data model(RTRDM)in real time database systems in paper of relational data model in real time database systems.This paper discusses all kinds of temporal relational operations on the basis of RTRDM,these of which can devolve upon traditional relational operations when lifespan of relationship is [Now,Now].
Relational algebra
Entity–relationship model
Data model (GIS)
Temporal database
Basis (linear algebra)
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Although the relational model for databases provides a great range of advantages over other data models, it lacks a comprehensive way to handle incomplete and uncertain data. Uncertainty in data values, however, is pervasive in all real-world environments and has received much attention in the literature. Several methods have been proposed for incorporating uncertain data into relational databases. However, the current approaches have many shortcomings and have not established an acceptable extension of the relational model. In this paper, we propose a consistent extension of the relational model. We present a revised relational structure and extend the relational algebra. The extended algebra is shown to be closed, a consistent extension of the conventional relational algebra, and reducible to the latter.
Relational algebra
Codd's theorem
Conjunctive query
Probabilistic database
Statistical relational learning
Data model (GIS)
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The connection between semantic database models and the relational model is formally investigated using the Iris Data Model, which has been implemented using relational database techniques. The results focus on properties of relational schemas that are translations of Iris schemas. Two new types of constraints, cross-product constraints and multiplicity constraints are introduced to characterize the relational translations of Iris schemas. The connection established between Iris and relational schemas also yields new, unexpected information about Iris schemas. In particular, a notion of equivalence of Iris schemas is defined using their relational translations, and a result is obtained on simplifying the type structure of Iris schemas.
Entity–relationship model
Conjunctive query
Data model (GIS)
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MatBase is a prototype data and knowledge base management expert intelligent system based on the Relational, Entity-Relationship, and (Elementary) Mathematical Data Models. Dyadic relationships are quite common in data modeling. Besides their relational-type constraints, they often exhibit mathematical properties that are not covered by the Relational Data Model. This paper presents and discusses the MatBase algorithm that assists database designers in discovering all non-relational constraints associated to them, as well as its algorithm for enforcing them, thus providing a significantly higher degree of data quality.
Entity–relationship model
Statistical relational learning
Base (topology)
Relational theory
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Abstract : The relational database model has become the most popular and widespread database model. Most current database systems are based upon or related to the relational model. However, the relational model is beset with significant limitations, pitfalls and deficiencies. The relational model can be substantially improved with graphical interfaces. To this end, the Graphics Language for Accessing Database (GLAD) can provide easy to use and learn graphics interfaces for the relational model. Data structures and algorithms for GLAD will be presented to extend the relational model. Keywords: Theses; Object orientated languages; Semantics; Syntax.
Entity–relationship model
Data model (GIS)
Relational algebra
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It is true to say that nearly all the current development work on databases involves relational database systems. Relational databases use a simple logical model which is further removed from the physical implementation than was the case for the network models examined in the previous chapter. A network database requires the user to navigate around the sets, as was illustrated in figure 11.10 for a sample CODASYL model. Often, in a relational database, the user merely defines what is required and the relational database decides how to achieve it. Because of this feature, relational databases are said to provide 'automatic navigation'.
Entity–relationship model
Logical data model
Feature (linguistics)
Spatiotemporal database
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Entity–relationship model
Nested set model
Codd's theorem
Data model (GIS)
Conjunctive query
Semantic data model
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The current fashion in database technology is the eschewing of relational databases in favor of object-oriented databases. C.J. Date and Hugh Darwen (1995) have issued a challenge for the industry to turn away from today's relational database products and to begin to develop and use databases conforming to the formal relational model. To meet the storage requirements of modern data, they propose to extend the notion of domains, and they claim that this expanded notion, when combined with the formal relational model, would be more than sufficient for the vast majority of the applications now being moved to object-oriented databases. This paper makes a brief survey of the relational model, object orientation in programming, and some of the present attempts to combine the two. It then presents ORR, a prototype implementation of a database system that incorporates a version of the expanded notion of domains into a near relational database.
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This research intend to communicate relational database model in libraryinformation- system software development with application using data in MARC format. The writer has followed the designing, implementation and testing process’ for 2 algorithms to be the main subject in this paper: MARC-to-Relational and Relational-to-MARC. MARC-to- Relational algorithm is applied when it needed data in MARC format to be processed in relational model. Conversely, Relational-to-MARC algorithm is applied to publish data from Relational database model in MARC Format . Keywords: MARC, library, relational database, OPAC.
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Data model (GIS)
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The concept of relational database and the design theory of relational model were introduced and the application of relational model to a pest database of afforestation was expounded in terms of relational data framework,relational operation and relational standards.
Afforestation
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