1073181
Keeping Secrets within a Family: Rediscovering Parnas
David Parnas wrote several papers in the 1970’s and 1980’s that are now considered classics. The concepts he advocated such as information hiding and use of abstract interfaces are generally accepted as the appropriate way to design nontrivial software systems. However, not all of what he proposed has been fully appreciated and assimilated into our practices. Many of his simple, elegant ideas have been lost amongst the hype surrounding the technologies and methods that have arisen in the past two decades. This paper examines Parnas’s ideas, especially his emphasis on program families, and proposes that college-level computing science and software engineering curricula should renew their attention to these very important principles and techniques and present them in the context of contemporary software development.
11623191
Mapping component specifications to Enterprise JavaBeans implementations
Component-based software development has become an important approach to building complex software systems. Much research focuses on component specification to achieve the advantages of the component-based approach in theory. Most of this research pays little attention to the mappings from component specifications to component implementations. However, the mappings are important because they determine whether the implementations perform satisfactorily to meet the specifications. After presenting a general approach to component specification and the technology of Enterprise JavaBeans (EJB) component model, this paper presents three approaches to mapping from component specification to EJB implementation. This paper uses a course registration system as an example to demonstrate the ideas. The approaches presented will be helpful to those who are working on the realizations of component systems.
12839905
Context sensitive vocabulary and its application in protein secondary structure prediction
Protein secondary structure prediction is an important step towards understanding the relation between protein sequence and structure. However, most current prediction methods use features difficult for biologists to interpret. In this paper, we present a new method that applies information retrieval techniques to solve the problem:we extract a context sensitive biological vocabulary for protein sequences and apply text classification methods to predict protein secondary structure. Experimental results show that our method performs comparably to the state-of-art methods. Furthermore, the context sensitive vocabularies can serve as a useful tool to discover meaningful regular expression patterns for protein structures.
10076000
IPR: automated interaction process reconciliation
Inter-organizational business processes usually require complex and time-consuming interactions between partners than simple interactions supported by WSDL. Automated reconciliation is essential to enable dynamic inter-organizational business collaboration. To the best of our knowledge, however, there is not a practical automated reconciliation algorithm available. In this paper, we propose a practical automated reconciliation algorithm, called IPR (interaction process reconciliation) based on Petri net, which is able to effectively facilitate dynamic interactions among trading partners in a peer-to-peer fashion. We implement a prototype IPR server in our lab, and evaluate our design by comprehensive experiments. Results show that IPR significantly outperforms existing approaches in terms of matching success rate, response time, and matching efficiency.
14342840
Solving Regular Path Queries
Regular path queries are a way of declaratively specifying program analyses as a kind of regular expressions that are matched against paths in graph representations of programs. These and similar queries are useful for other path analysis problems as well. This paper describes the precise specification, derivation, and analysis of a complete algorithm and data structures for solving regular path queries. We first show two ways of specifyingt he problem and deriving a high-level algorithmic solution, using predicate logic and language inclusion, respectively. Both lead to a set-based fixed-point specification. We then derive a complete implementation from this specification using Paige's methods that consist of dominated convergence, finite differencing, and real-time simulation. This formal derivation allows us to analyse the time and space complexity of the implementation precisely in terms of size parameters of the graph and the deterministic finite automaton that corresponds to the regular expression. In particular, the time and space complexity is linear in the size of the graph. We also note that the problem is PSPACE-complete in terms of the size of the regular expression. In applications such as program analysis, the size of the graph may be very large, but the size of the regular expression is small and can be considered a constant.
41398857
BIRD: Biomedical Information Integration and Discovery with Semantic Web Services
Biomedical research is now information intensive; the volume and diversity of new data sources challenges current database technologies. The development and tuning of database technologies for biology and medicine will maintain and accelerate the current pace for innovation and discovery. New promising application fields such as the Semantic Web and Semantic Web Services can leverage the potential of biomedical information integration and discovery, facing the problem of semantic heterogeneity of biomedical information sources in a variety of storage and data formats widely distributed both across the Internet and within individual organizations. In this paper, we present BIRD, a fully-fledged biomedical information integration solution that combines natural language analysis and semantically-empowered techniques to ascertain how the user needs can be best fit. Our approach is backed with a proof-of-concept implementation where the breakthrough and efficiency of integrating the biomedical publications database PubMed, the Database of Interacting Proteins (DIP) and the Munich Information Center for Protein Sequences (MIPS) has been tested.
36759557
Geometrical analysis of two sets of 3D correspondence data patterns
Given two sets of image correspondence data, the analysis of the corresponding rigid body transformation that accurately describes the object's motion in 3D space is a fundamental problem in image understanding. While many methods have been put forward to analyse 3D transformations, such methods do not make use of the vector distance between feature points and angular information as constraints to analyse transformation parameters. Current methods and algorithms have also suffered from the problems of lack of efficiency, sensitivity to noise, and multiplicity of solutions. In this paper we present a novel geometrical analysis of rigid body transformations and a novel algorithm to calibrate transformation parameters based on image correspondence. The algorithm is validated through experimental calibration and a comparison is made with calibration implemented by the least squares method. We demonstrate that the proposed algorithm works well in the presence of noise and that its performance is in general superior to algorithms based on the least squares method.