Data-driven Knowledge Research Articles

System Uncertainty Based Data-Driven Knowledge Acquisition

In the three-layered framework for knowledge discovery, it is necessary for technique layer to develop some data-driven algorithms, whose knowledge acquiring process is characterized by and hence advantageous for the unnecessity of prior domain knowledge or external information. System uncertainty is able to conduct data-driven knowledge acquiring process. It is crucial for such a knowledge acquiring framework to measure system uncertainty reasonably and precisely. Herein, in order to find a suitable measuring method, various uncertainty measures based on rough set theory are comprehensively studied: their algebraic characteristics and quantitative relations are disclosed; their performances are compared through a series of experimental tests; consequently, the optimal measure is determined. Then, a new data-driven knowledge acquiring algorithm is developed based on the optimal uncertainty measure and the Skowron’s algorithm for mining propositional default decision rules. Results of simulation experiments illustrate that the proposed algorithm obviously outperforms some other congeneric algorithms.

A study on integrating acoustic-phonetic information into lattice rescoring for automatic speech recognition

In this paper, a lattice rescoring approach to integrating acoustic-phonetic information into automatic speech recognition (ASR) is described. Additional information over what is used in conventional log-likelihood based decoding is provided by a bank of speech event detectors that score manner and place of articulation events with log-likelihood ratios that are treated as confidence levels. An artificial neural network (ANN) is then used to transform raw log-likelihood ratio scores into manageable terms for easy incorporation. We refer to the union of the event detectors and the ANN as knowledge module. A goal of this study is to design a generic framework which makes it easier to incorporate other sources of information into an existing ASR system. Another aim is to start investigating the possibility of building a generic knowledge module that can be plugged into an ASR system without being trained on specific data for the given task. To this end, the proposed approach is evaluated on three diverse ASR tasks: continuous phone recognition, connected digit recognition, and large vocabulary continuous speech recognition, but the data-driven knowledge module is trained with a single corpus and used in all three evaluation tasks without further training. Experimental results indicate that in all three cases the proposed rescoring framework achieves better results than those obtained without incorporating the confidence scores provided by the knowledge module. It is interesting to note that the rescoring process is especially effective in correcting utterances with errors in large vocabulary continuous speech recognition, where constraints imposed by the lexical and language models sometimes produce recognition results not strictly observing the underlying acoustic-phonetic properties.

3DM: Domain-oriented Data-driven Data Mining

Recent developments in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for knowledge discovery large-scale database. Data mining technology is a useful tool for this task. It is an emerging area of computational intelligence that offers new theories, techniques, and tools for processing large volumes of data, such as data analysis, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately,most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we would obey in a data mining process? What is the relationship between the prior knowledge of domain experts and the knowledgemind from data? In this paper, we will address these basic issues of data mining from the viewpoint of informatics [1]. Data is taken as a manmade format for encoding knowledge about the natural world. We take data mining as a process of knowledge transformation. A domain-oriented data-driven data mining (3DM) model based on a conceptual data mining model is proposed. Some data-driven data mining algorithms are also proposed to show the validity of this model, e.g., the data-driven default rule generation algorithm, data-driven decision tree pre-pruning algorithm and data-driven knowledge acquisition from concept lattice.

Mining bioprocess data: opportunities and challenges

Modern biotechnology production plants are equipped with sophisticated control, data logging and archiving systems. These data hold a wealth of information that might shed light on the cause of process outcome fluctuations, whether the outcome of concern is productivity or product quality. These data might also provide clues on means to further improve process outcome. Data-driven knowledge discovery approaches can potentially unveil hidden information, predict process outcome, and provide insights on implementing robust processes. Here we describe the steps involved in process data mining with an emphasis on recent advances in data mining methods pertinent to the unique characteristics of biological process data.

Guest Editors' Introduction: Distributed Data Mining--Framework and Implementations

It is now possible to gather and store incredible volumes of data, creating opportunities for large-scale data-driven knowledge discovery. Data mining technology has emerged as a means of performing this discovery, and the added dimension of distributed data mining increases this process's complexity.

Knowledge exploited by experts during software system design

High-level software design is characterized by incompletely specified requirements, no predetermined solution path, and by the integration of multiple domains of knowledge at various levels of abstraction. The application of data-driven knowledge rules characterizes expertise. A verbal protocol study describes these domains of knowledge and how experts exploit their rich knowledge during design. It documents how designers heavily rely on problem domain scenario simulations throughout solution development. These simulations trigger the inferences of new requirements and complete the requirement specification. Designers recognize partial solutions at various levels of abstraction in the design decomposition through the application of data-driven rules. Designers also rely heavily on simulations of their design solutions. but these are shallow, that is, limited to one level of abstraction in the solution. The findings also illustrate how designers capitalize on design methods, notations, and specialized software design schemas. Finally, the study describes how designers exploit powerful heuristics and personalized evaluation criteria to constrain the design process and select a satisfactory solution. Studies, such as this one, help map the road to understanding expertise in complex tasks.