Structuring technological information for technology roadmapping: Data mining approach

Most research on technology roadmapping has focused on its practical applications and the development of methods to enhance its operational process. Thus, despite a demand for well-supported, systematic information, little attention has been paid to how/which information can be utilised in technology roadmapping. Therefore, this paper aims at proposing a methodology to structure technological information in order to facilitate the process. To this end, eight methods are suggested to provide useful information for technology roadmapping: summary, information extraction, clustering, mapping, navigation, linking, indicators and comparison. This research identifies the characteristics of significant data that can potentially be used in roadmapping, and presents an approach to extracting important information from such raw data through various data mining techniques including text mining, multi-dimensional scaling and K-means clustering. In addition, this paper explains how this approach can be applied in each step of roadmapping. The proposed approach is applied to develop a roadmap of radio-frequency identification (RFID) technology to illustrate the process practically. © 2013 © 2013 Taylor & Francis.

Management and Analysis of Unstructured Construction Data Types

Compared with structured data sources that are usually stored and analyzed in spreadsheets, relational databases, and single data tables, unstructured construction data sources such as text documents, site images, web pages, and project schedules have been less intensively studied due to additional challenges in data preparation, representation, and analysis. In this paper, our vision for data management and mining addressing such challenges are presented, together with related research results from previous work, as well as our recent developments of data mining on text-based, web-based, image-based, and network-based construction databases.

Distributed flow algorithms for scalable similarity visualization

We describe simple yet scalable and distributed algorithms for solving the maximum flow problem and its minimum cost flow variant, motivated by problems of interest in objects similarity visualization. We formulate the fundamental problem as a convex-concave saddle point problem. We then show that this problem can be efficiently solved by a first order method or by exploiting faster quasi-Newton steps. Our proposed approach costs at most O(|ε|) per iteration for a graph with |ε| edges. Further, the number of required iterations can be shown to be independent of number of edges for the first order approximation method. We present experimental results in two applications: mosaic generation and color similarity based image layouting. © 2010 IEEE.

Kernel conditional quantile estimation via reduction revisited

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches. © 2009 IEEE.