Challenges, approaches and architecture for distributed process integration in heterogeneous environments

Integration and coordination of distributed processes remains a central challenge of construction information technology research. Extant technologies, while capable, are not yet scalable enough to enable rapid customization and instantiation for specific projects. Specifically, the heterogeneity of existing legacy sources together with firms’ range of approaches to process management makes deployment of integrated information technologies impractical. This paper reports on an architecture for distributed process integration named process connectors that addresses heterogeneity in a scalable manner. The process connectors architecture incorporates two key approaches that address heterogeneity over varying time scales. The SEEK: Scalable Extraction of Enterprise Knowledge toolkit is reviewed as a mechanism to discover semantically heterogeneous source data. The SEEK approach complements existing data integration methods for persistent sharing of information. To make use of shared data on a per project basis, a schedule mapping approach is presented that integrates firms’ diverse individual schedules in a unified representation. The schedule mapping approach allows integration of process views that have different levels of detail, while allowing participants to maintain their own view of the process. Collectively, SEEK and the schedule mapping approach facilitate a broad range of analyses to support coordination of distributed schedules. While this paper focuses primarily on schedule process integration, the process connectors architecture is viewed as providing a broad solution to discovery and integration of firms’ process data.     

A new approach for component’s port modeling in software architecture

The component’s interaction points with the external world play a fundamental role in the specification of an application’s architecture. Current software architecture approaches consider an interaction point as an atomic element in the specification of interconnections, despite the complexity of its structure and the attached behavior. It is not possible in current component models to deal separately with an element of an interaction point when such an element is needed alone for specifying a specific logic. To support such logic and the specification of a wide range of early ideas in the process of elaborating a software system, the Integrated Approach to Software Architecture (IASA) uses an interaction point model which provides facilities to manipulate any structural or behavioral element defining an interaction point. In addition, such facilities represent the fundamental foundation of the native support by IASA of Aspect Oriented Software Architectures (AOSA) specifications.     

Software factory principles, architecture, and experiments

The software factory concept which symbolizes a desired paradigm shift from labor-intensive software production to a more capital-intensive style in which substantial investments can be made at an acceptable risk level is discussed. Most traditional software environments emphasize support for producing code and associated documents. In a software factory, the focus shifts to coordinating information between producers and consumers so that the right person always has the right information at the right time. A CASE environment architecture and two factory experiments, one a prototype software factory environment for real-time system development, and the other a factory for exploring information logistics, are reviewed     

Hidden Markov Models for Text Categorization in Multi-Page Documents

In the traditional setting, text categorization is formulated as a concept learning problem where each instance is a single isolated document. However, this perspective is not appropriate in the case of many digital libraries that offer as contents scanned and optically read books or magazines. In this paper, we propose a more general formulation of text categorization, allowing documents to be organized as sequences of pages. We introduce a novel hybrid system specifically designed for multi-page text documents. The architecture relies on hidden Markov models whose emissions are bag-of-words resulting from a multinomial word event model, as in the generative portion of the Naive Bayes classifier. The rationale behind our proposal is that taking into account contextual information provided by the whole page sequence can help disambiguation and improves single page classification accuracy. Our results on two datasets of scanned journals from the Making of America collection confirm the importance of using whole page sequences. The empirical evaluation indicates that the error rate (as obtained by running the Naive Bayes classifier on isolated pages) can be significantly reduced if contextual information is incorporated.     

Software architecture awareness in long-term software product evolution

Software architecture has been established in software engineering for almost 40 years. When developing and evolving software products, architecture is expected to be even more relevant compared to contract development. However, the research results seem not to have influenced the development practice around software products very much. The architecture often only exists implicitly in discussions that accompany the development. Nonetheless many of the software products have been used for over 10, or even 20 years. How do development teams manage to accommodate changing needs and at the same time maintain the quality of the product? In order to answer this question, grounded theory study based on 15 semi-structured interviews was conducted in order to find out about the wide spectrum of architecture practices in software product developing organisations. Our results indicate that a chief architect or central developer acts as a ‘walking architecture’ devising changes and discussing local designs while at the same time updating his own knowledge about problematic aspects that need to be addressed. Architecture documentation and representations might not be used, especially if they replace the feedback from on-going developments into the ‘architecturing’ practices. Referring to results from Computer Supported Cooperative Work, we discuss how explicating the existing architecture needs to be complemented by social protocols to support the communication and knowledge sharing processes of the ‘walking architecture’.     

Supporting geographically-aware web document foraging and sensemaking

This paper reports on the development and application of strategies and tools for geographic information seeking and knowledge building that leverages unstructured text resources found on the web. Geographic knowledge building from unstructured web sources starts with web document foraging during which the quantity, scope and diversity of web-based information create incredible cognitive burdens on an analyst’s or researcher’s ability to judge information relevancy. Determining information relevancy is ultimately a process of sensemaking. In this paper, we present our research on visually supporting web document foraging and sensemaking. In particular, we present the Sense-of-Place (SensePlace) analytic environment. The scientific goal of SensePlace is to visually and computationally support analyst sensemaking with text artifacts that have potential place, time, and thematic relevance to an analytical problem through identification and visual highlighting of named entities (people, places, times, and organizations) in documents, automated inference to determine document relevance using stored knowledge, and a visual interface with coupled geographic map, timeline, and concept graph displays that are used to contextualize the contexts of potentially relevant documents. We present the results of a case study analysis using SensePlace to uncover potential population migration, geopolitical, and other infectious disease dynamics drivers for measles and other epidemics in Niger. Our analysis allowed us to demonstrate how our approach can support analysis of complex situations along (a) multi-scale geographic dimensions (i.e., vaccine coverage areas), (b) temporal dimensions (i.e., seasonal population movement and migrations), and (c) diverse thematic dimensions (effects of political upheaval, food security, transient movement, etc.).     

A text mining approach for automatic construction of hypertexts

The research on automatic hypertext construction emerges rapidly in the last decade because there exists a urgent need to translate the gigantic amount of legacy documents into web pages. Unlike traditional ‘flat’ texts, a hypertext contains a number of navigational hyperlinks that point to some related hypertexts or locations of the same hypertext. Traditionally, these hyperlinks were constructed by the creators of the web pages with or without the help of some authoring tools. However, the gigantic amount of documents produced each day prevent from such manual construction. Thus an automatic hypertext construction method is necessary for content providers to efficiently produce adequate information that can be used by web surfers. Although most of the web pages contain a number of non-textual data such as images, sounds, and video clips, text data still contribute the major part of information about the pages. Therefore, it is not surprising that most of automatic hypertext construction methods inherit from traditional information retrieval research. In this work, we will propose a new automatic hypertext construction method based on a text mining approach. Our method applies the self-organizing map algorithm to cluster some at text documents in a training corpus and generate two maps. We then use these maps to identify the sources and destinations of some important hyperlinks within these training documents. The constructed hyperlinks are then inserted into the training documents to translate them into hypertext form. Such translated documents will form the new corpus. Incoming documents can also be translated into hypertext form and added to the corpus through the same approach. Our method had been tested on a set of at text documents collected from a newswire site. Although we only use Chinese text documents, our approach can be applied to any documents that can be transformed to a set of index terms.     

Contextual web searches in Facebook using learning materials and discussion messages

The web is nowadays one of the main information sources, and information search is an important area in which many advances have been registered. One approach to improve web search results is to consider contextual information. Usually, information about context has been provided through user logs on previous searches or the monitoring of clicks on first results, but different approaches can be used in specific environments. In a web based learning environment, existing documents and exchanged messages could provide contextual information. So, the main goal of this work is to provide a contextual web search engine based on shared documents and messages posted in a social network used for collaborative learning. Contextual search is provided through query expansion using learning documents (material provided by the teacher) and discussion messages (posts, links and comments that result from the participants’ interactions). A prototype was implemented and used in a learning scenario to acquire the context in a learning community. The proposed approach makes the context acquisition faster and more dynamic as it considers an automatic approach over text processing of documents and discussions. In addition, the results of the query engine with and without the contextual information were compared and the proposed approach using contextual information showed improvements in the precision of the results.     

A Multilingual Text Mining Approach Based on Self-Organizing Maps

This paper describes our work on developing a language-independent technique for discovery of implicit knowledge from multilingual information sources. Text mining has been gaining popularity in the knowledge discovery field, particularity with the increasing availability of digital documents in various languages from all around the world. However, currently most text mining tools mainly focus only on processing monolingual documents (particularly English documents): little attention has been paid to apply the techniques to handle the documents in Asian languages, and further extend the mining algorithms to support the aspects of multilingual information sources. In this work, we attempt to develop a language-neutral method to tackle the linguistics difficulties in the text mining process. Using a variation of automatic clustering techniques, which apply a neural net approach, namely the Self-Organizing Maps (SOM), we have conducted several experiments to uncover associated documents based on a Chinese corpus, Chinese-English bilingual parallel corpora, and a hybrid Chinese-English corpus. The experiments show some interesting results and a couple of potential paths for future work in the field of multilingual information discovery. Besides, this work is expected to act as a starting point for exploring the impacts on linguistics issues with the machine-learning approach to mining sensible linguistics elements from multilingual text collections.     

On the architecture of a human-centered CAD agent system

With an increasing role of computers in the design process, a strong foundation of the basic principles for building CAD systems is extremely necessary. This work describes the architecture of a CAD system from the perspective of the capabilities required to build a human centered CAD system. These capabilities have been reported as task-sharing, communication, transparency, design rationale and cognition. Existing systems for engineering design have been analyzed from the perspective of these capabilities and an agent-based system previously reported in literature has been extended to a new architecture to support three of the aforementioned capabilities. The proposed architecture is an amalgamation of multi-agent systems, the ACT-R cognitive architecture and fuzzy rule based Petri nets. The salient advantage of this architecture is that it focuses on the capabilities of design rationale, transparency and cognition together—something which has been missing from all the current works in literature and is unique to the domain of application of the software system, which in this case is engineering design. The other capabilities namely task sharing and communication can be incorporated from lessons learnt from human–computer interaction research. The effectiveness of the proposed architecture is demonstrated by a hypothetical design case along with its implementation.     

企业应用软件架构分析

通过对企业业务体系的根本需求进行分析,描述了企业业务及其支持软件的特点.从时间和空间两个维度上对企业软件架构进行了描述,通过分析企业应用软件架构与企业业务改进过程的关系,对架构的开发和部署过程提出了一些建议.     

A closed-loop control architecture for CAM accounting for shop floor uncertainties

The primary focus of this paper is to demonstrate an enhanced closed-loop computer-aided manufacturing (CAM) architecture using a Petri net based analysis mechanism, which incorporates shop floor uncertainties to generate process plans and alternative processing sequences under dynamic conditions such as machine failures. Modern CAM software provide efficient, error-free, and user-friendly interfaces to develop processing sequences for manufacturing tasks. However, their effective scope is still limited from the post-process planning stage to the pre-machining stage. These types of software fail to incorporate the variability encountered in an actual shop floor to develop effective process plans. This is caused due to CAM software’s limitations in input handling mechanisms, limited simulation functionality, and inability to handle complex process variability from the shop floor. In this paper, CAM functionality is augmented with Petri net models to capture alternative process plans and shop floor variability arising due to equipment failures, repairs, and setups. The output from the analysis of the Petri net model provides a better understanding of the process characteristics and provides information to the CAM system to adapt to shop floor variability. The resulting information from the Petri net model allows the CAM system to compare alternative process plans while taking shop floor information into account. This mechanism can result in better decisions, reduced processing times, and better overall resource utilization, and provide alternative process plans based on current resource information.     

一种支持设计时软件重用的反射式软件体系结构及其形式化研究

软件体系结构在软件重用中有着特殊的意义。缺乏显式的描述并使用支持体系结构重用过程的信息和缺乏有效的重用方法是软件体系结构难以重用最根本的原因。在软件设计阶段,将元信息、元建模、反射和软件体系结构结合起来,构造了一种支持软件体系结构重用的反射式软件体系结构。给出了基于反射式软件体系结构的软件体系结构重用过程。给出了反射式软件体系结构的具体化过程的基本原理和具体化操作的Object-Z形式化描述,即创建元组件、创建元连接器、创建元组合件操作的形式化描述。给出了基于反射式软件体系结构的软件体系结构重用的支撑工具的设计过程。     

Information discovery across multiple streams

In this paper we address the issue of continuous keyword queries on multiple textual streams and explore techniques for extracting useful information from them. The paper represents, to our best knowledge, the first approach that performs keyword search on a multiplicity of textual streams. The scenario that we consider is quite intuitive; let’s assume that a research or financial analyst is searching for information on a topic, continuously polling data from multiple (and possibly heterogeneous) text streams, such as RSS feeds, blogs, etc. The topic of interest can be described with the aid of several keywords. Current filtering approaches would just identify single text streams containing some of the keywords. However, it would be more flexible and powerful to search across multiple streams, which may collectively answer the analyst’s question. We present such model that takes in consideration the continuous flow of text in streams and uses efficient pipelined algorithms such that results are output as soon as they are available. The proposed model is evaluated analytically and experimentally, where the Enron dataset and a variety of blog datasets are used for our experiments.     

Usability and software architecture

The role of software architecture with respect to usability has evolved over the past 20 years. The architectures of the 1980s and early 1990s assumed that usability was primarily a property of the presentation of information. Therefore, simply separating the presentation from the dialogue and application made it easy to modify that presentation after user testing. A more popular belief in the 1990s was that usability concerns greatly affected system functionality as well as the presentation. This emphasis took attention away from architectural support (beyond separation). Achieving the correct functionality for a given system became paramount. It is our observation that even if presentation and functionality of a system are well designed, the usability of a system can be greatly compromised if the underlying architecture does not support human concerns beyond modifiability. This paper will present a new role for software architecture in usability, preliminary research and practice stemming from this role and a research agenda for the future.