IBM

About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.

ALMI: an application level multicast infrastructure

Abstract: The IP multicast model allows scalable and efficient multi-party communication, particularly for groups of large size. However, deployment of IP multicast requires substantial infrastructure modifications and is hampered by a host of unresolved open problems. To circumvent this situation, we have designed and implemented ALMI, and application level group communication middleware, which allows accelerated application deployment and simplified network configuration, without the need of network infrastructure support. ALMI is tailored toward support of multicast groups of relatively small size (several 10s of members) with many to many semantics. Session participants are connected via a virtual multicast tree, which consists of unicast connections between end hosts and is formed as a minimum spanning tree (MST) using application-specific performance metric. Using simulation, we show that the performance penalties, introducted by this shift of multicast to end systems, is a relatively small increase in traffic load and that ALMI multicast trees approach the efficiency of IP multicast trees. We have also implemented ALMI as a Java based middleware package and performed experiments over the Internet. Experimental results show that ALMI is able to cope with network dynamics and keep the multicast tree efficient.

Advances in software inspections

Abstract: Software inspection is a method of static testing to verify that software meets its requirements. It engages the developers and others in a formal process of investigation that usually detects more defects in the product-and at lower cost-than does machine testing. Studies and experiences are presented which enhance the use of the inspection process and improve its contribution to development of defect-free software on time and at lower cost. Examples of benefits are cited followed by descriptions of the inspection process and some methods of obtaining the enhanced results. Users of the method report very significant improvements in quality that are accompanied by lower development costs and greatly reduced maintenance efforts. Excellent results have been obtained by small and large organizations in all aspects of new development as well as in maintenance. There is some evidence that developers who participate in the inspection of their own product actually create fewer defects in subsequent work. Because inspections formalize the development process, productivity-enhancing and quality-enhancing tools can be adopted more easily and rapidly.

The Emergence of Service Science: Toward Systematic Service Innovations to Accelerate Co‐Creation of Value

Abstract: The current growth of the service sector in global economies is unparalleled in human history—by scale and speed of labor migration. Even large manufacturing firms are seeing dramatic shifts in percent revenue derived from services. The need for service innovations to fuel further economic growth and to raise the quality and productivity levels of services has never been greater. Services are moving to center stage in the global arena, especially knowledge-intensive business services aimed at business performance transformation. One challenge to systematic service innovation is the interdisciplinary nature of service, integrating technology, business, social, and client (demand) innovations. This paper describes the emergence of service science, a new interdisciplinary area of study that aims to address the challenge of becoming more systematic about innovating in service.

Mining Text Data

Abstract: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.