Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

scholarly journals Knowledge Acquisition and Representation for High-Performance Building Design: A Review for Defining Requirements for Developing a Design Expert System

2021 ◽  
Vol 13 (9) ◽  
pp. 4640
Author(s):  
Seung-Yeoun Choi ◽  
Sean-Hay Kim
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Data Analytics ◽  
High Performance ◽  
Problem Definition ◽  
Design Expert ◽  
Decision Support Process ◽  
High Performance Building

New functions and requirements of high performance building (HPB) being added and several regulations and certification conditions being reinforced steadily make it harder for designers to decide HPB designs alone. Although many designers wish to rely on HPB consultants for advice, not all projects can afford consultants. We expect that, in the near future, computer aids such as design expert systems can help designers by providing the role of HPB consultants. The effectiveness and success or failure of the solution offered by the expert system must be affected by the quality, systemic structure, resilience, and applicability of expert knowledge. This study aims to set the problem definition and category required for existing HPB designs, and to find the knowledge acquisition and representation methods that are the most suitable to the design expert system based on the literature review. The HPB design literature from the past 10 years revealed that the greatest features of knowledge acquisition and representation are the increasing proportion of computer-based data analytics using machine learning algorithms, whereas rules, frames, and cognitive maps that are derived from heuristics are conventional representation formalisms of traditional expert systems. Moreover, data analytics are applied to not only literally raw data from observations and measurement, but also discrete processed data as the results of simulations or composite rules in order to derive latent rule, hidden pattern, and trends. Furthermore, there is a clear trend that designers prefer the method that decision support tools propose a solution directly as optimizer does. This is due to the lack of resources and time for designers to execute performance evaluation and analysis of alternatives by themselves, even if they have sufficient experience on the HPB. However, because the risk and responsibility for the final design should be taken by designers solely, they are afraid of convenient black box decision making provided by machines. If the process of using the primary knowledge in which frame to reach the solution and how the solution is derived are transparently open to the designers, the solution made by the design expert system will be able to obtain more trust from designers. This transparent decision support process would comply with the requirement specified in a recent design study that designers prefer flexible design environments that give more creative control and freedom over design options, when compared to an automated optimization approach.

scholarly journals Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

2020 ◽  
Vol 3 (3) ◽  
pp. 34
Author(s):  
Antoni Escobet ◽  
Teresa Escobet ◽  
Joseba Quevedo ◽  
Adoración Molina
Keyword(s):  
Natural Gas ◽  
Data Analytics ◽  
Liquefied Natural Gas ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Sensor Data ◽  
Gas Consumption ◽  
Using Data ◽  
Maintenance Plan ◽  
Remaining Time

This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.

A PROPOSED ARCHITECTURE FOR SELF-ADAPTIVE EXPERT SYSTEMS

2009 ◽  
Vol 19 (02) ◽  
pp. 213-248 ◽  
Author(s):  
TSUNG-TENG CHEN ◽  
CHENG-SEEN HO
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Domain Knowledge ◽  
Semantic Networks ◽  
Inference Process ◽  
Operating Environment ◽  
Traditional System ◽  
User Input ◽  
Adaptive Capability ◽  
Self Adaptive

The pre-built knowledge of traditional expert systems is only capable of limited responses to changes in the operating environment. If the data input is imperfect, a traditional system may fail to reach any rational conclusions. In this paper, we introduce the concept of self-adaptability to the inference process of an expert system, and propose a model that is capable of handling unexpected user input effectively and efficiently. Such a system can formulate operational knowledge on the move for inference. With this self-adaptive capability, an expert system can reach useful conclusions, even when the input data is insufficient. The architecture of the proposed system encodes domain knowledge with semantic networks. It also defines four types of adaptation, namely, condition knowledge adaptation, operational knowledge adaptation, conclusion knowledge adaptation, and presentation adaptation, and focuses on how the first three contribute to the adaptive capability of the system. In addition, to enable a self-adaptive expert system to effectively produce better conclusions, two entropy-based measuring mechanisms are proposed: one minimizes the information loss during knowledge adaptation, while the other selects the best attribute relation during the generation of operational knowledge. We have proved that a self-adaptive expert system based on this architecture can always reach a regular conclusion or an abstract conclusion, which is a more meaningful conclusion by automatically modifying its operational knowledge in response to user feedback during the inference process, even in unexpected situations.

Well log data analytics: overview of applications to improve subsurface characterisation

2019 ◽  
Vol 59 (2) ◽  
pp. 874
Author(s):  
Irina Emelyanova ◽  
Chris Dyt ◽  
M. Ben Clennell ◽  
Jean-Baptiste Peyaud ◽  
Marina Pervukhina
Keyword(s):  
Data Analytics ◽  
Linear Prediction ◽  
Prediction Models ◽  
Well Logs ◽  
Rock Properties ◽  
Learning Approaches ◽  
Rule Based ◽  
Log Data ◽  
Expert Rule ◽  
Combining Data

Wireline log datasets complemented with core measurements and expert interpretation are vital for accurate reservoir characterisation. In many cases, effective use of this information for predicting rock properties requires application of advanced data analytics (DA) techniques. We developed non-linear prediction models by combining data- and knowledge-driven methods. These models were used for predicting total organic carbon and electro-facies from basic wireline logs. Four DA approaches were utilised: unsupervised, supervised, semi-supervised and expert rule based. The unsupervised approach implements ensemble clustering for detecting variations in sedimentary sequences of the subsurface. The supervised approach predicts rock properties from well logs by applying ensemble learning that requires core data measurements. The semi-supervised approach builds a decision tree for iterative clustering of well logs to locate a specific facies and uses criteria determined by a petrophysicist for making decisions at each tree node whether to continue or stop the partitioning. The expert rule based approach combines clustering techniques at individual wells with an expert’s methodology of interpreting facies to determine field-wide rock characterisation. Here we overview the developed models and their applications to log data from offshore and onshore Australian wells. We discuss the deep thinking–shallow learning versus shallow thinking–deep learning approaches in reservoir modelling and highlight the importance of close collaboration of data analysts with domain experts.

Parallel Processing Applications in Expert Systems in Power Systems

1995 ◽  
Vol 32 (2) ◽  
pp. 154-162
Author(s):  
J. P. Wang ◽  
J. Trecat
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Parallel Processing ◽  
Power Systems ◽  
Expert Systems ◽  
Inference Method ◽  
Problem Size ◽  
Rule Based ◽  
Model Based

Parallel processing applications in expert systems in power sytems Parallel processing is widely used to reduce computation times. In its application to non-numerical problems, such as expert systems, inference method and problem size must be considered. A fault diagnosis expert system is considered as an example, with either a model-based or a rule-based inference, applied to power systems of various sizes.

scholarly journals Rule Based and Supervisory Training Approach To Develop Expert System Tool For Detecting Lung Cancer Disease

2010 ◽  
Vol 9 (1) ◽  
pp. 1-11
Author(s):  
K. Balachandran ◽  
R. Anitha
Keyword(s):  
Lung Cancer ◽  
Expert System ◽  
Expert Systems ◽  
Medical Attention ◽  
Cancer Disease ◽  
Rule Based ◽  
Knowledge Based ◽  
Training Approach ◽  
Dreaded Disease ◽  
Rule Based Approach

Knowledge-based expert systems, or expert systems, use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Lung cancer is one of the dreaded disease in the modern era. It is responsible for the most cancer deaths in both men and women throughout the world. Early diagnosis and timely treatment are imperative for the cure. Longevity and cure depends on early detection. This paper gives on insight to identify the forget group of people who are suffering or susceptible to suffer lung cancer disease. Seeking proper medical attention con be initiated based on the findings. Expert system tool developed, to find this target group based on the non-clinical parameters. Symptoms and risk factors associated with Lung cancer ore token as the basis of this study. This expert system basically works on the rule based approach to collect the data. Then Supervisory learning approach is used to infer the basic data. Once sufficient knowledge base is generated the system can be made to adopt in unsupervised learning mode.

ACCESSING DATA MINING RULES THROUGH EXPERT SYSTEMS

2002 ◽  
Vol 01 (04) ◽  
pp. 657-672 ◽  
Author(s):  
BASILIS BOUTSINAS
Keyword(s):  
Data Mining ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Discovery ◽  
Domain Knowledge ◽  
Research Area ◽  
General Purpose ◽  
Related Data ◽  
Different Types

Data mining is an emerging research area that develops techniques for knowledge discovery in huge volumes of data. Usually, data mining rules can be used either to classify data into predefined classes, or to partition a set of patterns into disjoint and homogeneous clusters, or to reveal frequent dependencies among data. The discovery of data mining rules would not be very useful unless there are mechanisms to help analysts access them in a meaningful way. Actually, documenting and reporting the extracted knowledge is of considerable importance for the successful application of data mining in practice. In this paper, we propose a methodology for accessing data mining rules, which is based on using an expert system. We present how the different types of data mining rules can be transformed into the domain knowledge of any general-purpose expert system. Then, we present how certain attribute values given by the user as facts and/or goals can determine, through a forward and/or backward chaining, the related data mining rules. In this paper, we also present a case study that demonstrates the applicability of the proposed methodology.

HIDES: A Computer-Based Herbicide Injury Diagnostic Expert System

2005 ◽  
Vol 19 (2) ◽  
pp. 486-491 ◽  
Author(s):  
Jingkai Zhou ◽  
Calvin G. Messersmith ◽  
Janet D. Harrington
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Case Based Reasoning ◽  
Specific Information ◽  
Rule Based ◽  
Source Determination ◽  
Step Procedure ◽  
Computer Based ◽  
Herbicide Use ◽  
Case Based

Diagnosis of herbicide injury can be complex because of the large number and interaction of factors leading to herbicide injury. Computer-based expert systems have great potential to assist users, particularly nonexperts, in accurate diagnosis of herbicide injury. Rule-based and case-based reasoning are the most widely used forms of expert systems, and each system has strengths and limitations. Approaches that integrate rule-based and case-based reasoning may augment the positive aspects of the two reasoning methods and simultaneously minimize their negative aspects. The Herbicide Injury Diagnostic Expert System (HIDES) integrates rule-based and case-based reasoning and uses field-specific information, injury symptoms, herbicide use history, and herbicide information to diagnose crop injury from herbicides. The HIDES program uses a set of rules to identify suspect herbicide(s) that is the candidate for causing the observed injury and possible sources of the suspect herbicide(s). Case-based reasoning is used to propose a probable cause of injury by making an analogy to previously solved cases. A four-step procedure is followed when using HIDES: information collection, suspect herbicide identification, suspect herbicide source determination, injury reason suggestion, and knowledge accumulation.

An Expert System Approach to Workload Reduction

1986 ◽  
Vol 30 (7) ◽  
pp. 702-706
Author(s):  
Gilbert G. Kuperman ◽  
Denise L. Wilson
Keyword(s):  
Expert System ◽  
High Resolution ◽  
Expert Systems ◽  
System Approach ◽  
Application Area ◽  
Research Facility ◽  
Rule Based ◽  
High Resolution Radar ◽  
Workload Reduction

A research facility for the development of rule-based, expert systems was developed. Workload reduction was selected as an application area for demonstration. The specific crew function demonstrated was the employment of a high resolution radar in a navigation update task.

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