This paper presents a methodology for designing proper nesting structures of user-defined types in object-relational databases. Briefly, we envision that users model a real-world application by using ...the EER model, which results in an EER schema. Our algorithm then uses the theory we developed for nested relations to generate scheme trees from the EER schema. We shall prove that the resulting scheme trees have exactly the same information content as the EER schema, and the scheme-tree instances over the resulting scheme trees do not store information redundantly. Finally, the scheme trees are transformed to Oracle Database 10
g nested object types for implementation. The algorithm in this paper forms the core of a computerized object-relational database design tool we shall develop in the future.
In this paper, we revisit a number of classical formal meta-properties that have been used in the conceptual modeling and ontology engineering literature to provide finer-grained distinctions among ...the category of Object Types. These distinctions constitute an essential part of relevant existing approaches, in particular, the ontology-driven conceptual modeling language OntoUML, and the ontology and taxonomy evaluation methodology OntoClean. The idea in this paper is to investigate the interaction between these meta-properties and Derived Object Types, i.e., Object Types which extensions are dynamically inferred via Derivation Rules. The contributions here are two-fold: firstly, we revisit two classical Derivation Patterns and prove a number of results that can be used to infer the modal meta-properties of Derived Types from those of the types participating in the associated derivation rules; secondly, we demonstrate how these results can be applied in the automated support for model construction in OntoUML.
RGB-D cameras play an increasingly important role in localization and autonomous navigation of mobile robots. Reasonably priced commercial RGB-D cameras have recently been developed for operation in ...greenhouse and outdoor conditions. They can be employed for different agricultural and horticultural operations such as harvesting, weeding, pruning and phenotyping. However, the depth information extracted from the cameras varies significantly between objects and sensing conditions. This paper presents an evaluation protocol applied to a commercially available Fotonic F80 time-of-flight RGB-D camera for eight different object types. A case study of autonomous sweet pepper harvesting was used as an exemplary agricultural task. Each of the objects chosen is a possible item that an autonomous agricultural robot must detect and localize to perform well. A total of 340 rectangular regions of interests (ROI) were marked for the extraction of performance measures of point cloud density, and variability around center of mass, 30-100 ROIs per object type. An additional 570 ROIs were generated (57 manually and 513 replicated) to evaluate the repeatability and accuracy of the point cloud. A statistical analysis was performed to evaluate the significance of differences between object types. The results show that different objects have significantly different point density. Specifically metallic materials and black colored objects had significantly less point density compared to organic and other artificial materials introduced to the scene as expected. The point cloud variability measures showed no significant differences between object types, except for the metallic knife that presented significant outliers in collected measures. The accuracy and repeatability analysis showed that 1-3 cm errors are due to the the difficulty for a human to annotate the exact same area and up to ±4 cm error is due to the sensor not generating the exact same point cloud when sensing a fixed object.
Scala unifies concepts from object and module systems by allowing for objects with type members which are referenced via path-dependent types. The Dependent Object Types (DOT) calculus of Amin et al. ...models only this core part of Scala, but does not have many fundamental features of Scala such as strict and mutable fields. Since the most commonly used field types in Scala are strict,the correspondence between DOT and Scala is too weak for us to meaningfully prove static analyses safe for Scala by proving them safe for DOT.
A DOT calculus that can support strict and mutable fields together with constructors that do field initialization would be more suitable for analysis of Scala. Toward this goal, we present κDOT, an extension of DOT that supports constructors and field mutation and can emulate the different types of fields in Scala. We have proven κDOT sound through a mechanized proof in Coq. We present the key features of κDOT and its operational semantics and discuss work-in-progress toward making κDOT fully strict.
DOT (Dependent Object Types) is an object calculus with path-dependent types and abstract type members, developed to serve as a theoretical foundation for the Scala programming language. As yet, DOT ...does not model all of Scala's features, but a small subset. We present the calculus DIF (DOT with Implicit Functions), which extends the set of features modelled by DOT to include implicit functions, a feature of Scala to aid modularity of programs. We show type safety of DIF, and demonstrate that the generic programming focused use cases for implicit functions in Scala are also expressible in DIF.
The Dependent Object Types (DOT) calculus formalizes key features of Scala. The D<: calculus is the core of DOT. To date, presentations of D<: have used declarative, as opposed to algorithmic, typing ...and subtyping rules. Unfortunately, algorithmic typing for full D<: is known to be an undecidable problem.
We explore the design space for a restricted version of D<: that has decidable typechecking. Even in this simplified D<:, algorithmic typing and subtyping are tricky, due to the "bad bounds" problem. The Scala compiler bypasses bad bounds at the cost of a loss in expressiveness in its type system. Based on the approach taken in the Scala compiler, we present the Step Typing and Step Subtyping relations for D<:. These relations are sound and decidable. They are not complete with respect to the original D<: typing rules.
The value of knowledge is not in line with its economic valuation. Due to how economic value is perceived in the industrial systems of the 19th and 20th century, corporate bookkeeping systems do not ...explicitly account for knowledge. In centrally guided systems economic value was linked with material commodities created by physical labour, whereas in capitalist countries the value of immaterial assets was denied by accountancy foundations. While industrial systems have transformed into knowledge economies, bookkeeping systems still rely on concepts of industrial economies. Knowledge economies being complex systems and enterprises, as a consequence, struggle when processing service requests. Knowledge management activities are not targeted to mastering complexity, but in stead are focused on representing it. Representation of knowledge is often human-based suffering from randomness and limiting scalability in time and space. IT approaches focus on representation of logic in legacy systems that are closed and rigid and not very adequate to convey and maintain knowledge. Enterprise logic is represented in text documents, calculation sheets, flow charts and similar techniques that do no justness to the characteristics of complexity. So, our views of knowledge and knowledge management do not seem to be in a satisfactory shape and need revision. Old-fashioned concepts underlying bookkeeping systems need to be synchronised with the knowledge economy. At the same time, the value of knowledge needs to be capitalised. The current accumulation of inefficiencies offers an expected but much needed and excellent opportunity to induce a paradigm change to accomplish this. The paradigm of functional object-types delivers a broader and deeper view of knowledge relating it to complexity. It provides a theory how to characterise and structure complex logic and render knowledge usable and adaptable for the transformation function of an enterprise. In addition to scientific advances, the first practical results reveal that the paradigm, including its Match(TM) Technology instruments, yields unprecedented achievements that are unthinkable with classical business improvement approaches. It showcases formidable time reductions in the transformation model, lower development and maintenance costs and higher quality of enterprise logic. Its 7th generation application-by-specification development is revolutionary short and elastic.
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