Technology-driven roadmaps for identifying new product/market opportunities: Use of text mining and quality function deployment

A technology roadmap (TRM), an approach that is applied to the development of an emerging technology to meet business goals, is one of the most frequently adopted tools to support the process of technology innovation. Although many studies have dealt with TRMs that are designed primarily for a market-driven technology planning process, a technology-driven TRM is far less researched than a market-driven one. Furthermore, approaches to a technology-driven roadmap using quantitative technological information have rarely been studied. Thus, the aim of this research is to propose a new methodological framework to identify both profitable markets and promising product concepts based on technology information. This study suggests two quality function deployment (QFD) matrices to draw up the TRM in order to find new business opportunities. A case study is presented to illustrate the proposed approach using patents on the solar-lighting devices, which is catching on as a high-tech way to prevent environmental pollution and reduce fuel costs.     

Computational linguistics for metadata building (CLiMB): using text mining for the automatic identification,categorization, and disambiguation of subject terms for image metadata

In this paper, we present a system using computational linguistic techniques to extract metadata for image access. We discuss the implementation, functionality and evaluation of an image catalogers’ toolkit, developed in the Computational Linguistics for Metadata Building (CLiMB) research project. We have tested components of the system, including phrase finding for the art and architecture domain, functional semantic labeling using machine learning, and disambiguation of terms in domain-specific text vis a vis a rich thesaurus of subject terms, geographic and artist names. We present specific results on disambiguation techniques and on the nature of the ambiguity problem given the thesaurus, resources, and domain-specific text resource, with a comparison of domain-general resources and text. Our primary user group for evaluation has been the cataloger expert with specific expertise in the fields of painting, sculpture, and vernacular and landscape architecture.

Long-term evaluation of three satellite ocean color algorithms for identifying harmful algal blooms (Karenia brevis) along the west coast of Florida: A matchup assessment

We present a simple algorithm to identify Karenia brevis blooms in the Gulf of Mexico along the west coast of Florida in satellite imagery. It is based on an empirical analysis of collocated matchups of satellite and in situ measurements. The results of this Empirical Approach is compared to those of a Bio-optical Technique - taken from the published literature - and the Operational Method currently implemented by the NOAA Harmful Algal Bloom Forecasting System for K. brevis blooms. These three algorithms are evaluated using a multi-year MODIS data set (from July, 2002 to October, 2006) and a long-term in situ database. Matchup pairs, consisting of remotely-sensed ocean color parameters and near-coincident field measurements of K. brevis concentration, are used to assess the accuracy of the algorithms. Fair evaluation of the algorithms was only possible in the central west Florida shelf (i.e. between 25.75°N and 28.25°N) during the boreal Summer and Fall months (i.e. July to December) due to the availability of valid cloud-free matchups. Even though the predictive values of the three algorithms are similar, the statistical measure of success in red tide identification (defined as cell counts in excess of 1.5 × 10⁴ cells L⁻¹) varied considerably (sensitivity—Empirical: 86%; Bio-optical: 77%; Operational: 26%), as did their effectiveness in identifying non-bloom cases (specificity—Empirical: 53%; Bio-optical: 65%; Operational: 84%). As the Operational Method had an elevated frequency of false-negative cases (i.e. presented low accuracy in detecting known red tides), and because of the considerable overlap between the optical characteristics of the red tide and non-bloom population, only the other two algorithms underwent a procedure for further inspecting possible detection improvements. Both optimized versions of the Empirical and Bio-optical algorithms performed similarly, being equally specific and sensitive (~ 70% for both) and showing low levels of uncertainties (i.e. few cases of false-negatives and false-positives: ~ 30%)—improved positive predictive values (~ 60%) were also observed along with good negative predictive values (~ 80%).