Rule Database Research Articles

A TCAM-based Caching Architecture Framework for Packet Classification

Packet Classification is the enabling function for performing many networking applications like Integrated Services, Differentiated Services, Access Control/Firewalls, and Intrusion Detection. To cope with high-speed links and ever-increasing bandwidth requirements, time-efficient solutions are needed for which Ternary Content Addressable Memories (TCAMs) are popularly used. However, high cost, heavy power consumption, and poor scalability limit their use in many commercial switches. In this work, an efficient framework for caching the packet classification rules on TCAMs in accordance with traffic characteristics is proposed. The proposed design will have a two-level classification engine in which level-1 is a TCAM classifier with a smaller rule capacity and level-2 is a software classifier. The classifiers are assisted by a rule update engine that monitors the rule temporal behavior and performs timely updates of the rules onto level-1. Crucial challenges with respect to the proposed framework design are defined and addressed effectively in this work. Simulation results shows that the architecture can achieve a throughput of 250 Gbps on average by caching only 10% of the total rules for rule databases of sizes 10,000. The proposed architecture, to the best of our knowledge, is the only traffic-aware architecture using TCAMs that provides a completely deployable framework and also can scale for speeds beyond 250 Gbps (OC-1920 and beyond).

Using Affiliation Rules-based Data Mining Technique in Referral System

Referral techniques are normally employed in internet business applications. Existing frameworks prescribe things to a particular client according to client inclinations and former high evaluations. Quite a number of methods, such as cooperative filtering and content-based methodologies, dominate the architectural design of referral frameworks. Many referral schemes are domain-specific and cannot be deployed in a general-purpose setting. This study proposes a two-dimensional (User × Item)-space multimode referral scheme, having an enormous client base but few articles on offer. Additionally, the design of the referral scheme is anchored on the and articles, as expressed by a particular client, and is a combination of affiliation rules mining and the content-based method. The experiments used the dataset of MovieLens, consisting of 100,000 motion pictures appraisals on a size of 1-5, from 943 clients on 1,682 motion pictures. It utilised a five-overlap cross appraisal on a (User × Item)-rating matrix with 12 articles evaluated by a minimum of 320 clients. A total of 16 rules were generated for both and articles, at 35% minimum support and 80% confidence for the articles and 50% similitude for the . Experimental results showed that the anticipated appraisals in denary give a better rating than other measures of exactness. In conclusion, the proposed algorithm works well and fits on two dimensional -space with articles that are significantly fewer than users, thus making it applicable and effective in a variety of uses and scenarios as a general-purpose utility.

Mining Fuzzy Common Sequential Rules with Fuzzy Time-Interval in Quantitative Sequence Databases

There are two kinds of sequential rules. They are classical sequential rules and common sequential rules. The common sequential rules present the relationship between unordered itemsets in which all the items in the antecedent part have to appear before the ones in the consequent part. All existing algorithms for mining common sequential rules can not apply to quantitative sequence databases. Furthermore, the common sequential rules found so far did not yet reveal the time gap about the apperance of itemsets in its antecedent and consequent parts. The purpose of this article is to overcome the two disadvantages mentioned above. Specifically, the article proposes an algorithm called IFERMiner to discover common sequential rules in quantitative sequence databases, where the time gap about appearance of two attribute sets in its antecedent and consequent parts is taken account. This algorithm was developed from the ERMiner algorithm that is the most efficient algorithm to discover common sequential rules in transactional sequence databases now. The computational complexity of the IFERMiner algorithm is also shown in the article and it is polynomial. The FCSI rules found out by the IFERMiner algorithm are useful for marketing domain and market analysis.

Text Classification and Tagging of United States Army Ground Vehicle Fault Descriptions in Support of Data-Driven Prognostics

The manner in which a prognostics problem is framed is critical for enabling its solution by the proper method. Recently, data-driven prognostics techniques have demonstrated enormous potential when used alone, or as part of a hybrid solution in conjunction with physics-based models. Historical maintenance data constitutes a critical element for the use of a data-driven approach to prognostics, such as supervised machine learning. The historical data is used to create training and testing data sets to develop the machine learning model. Categorical classes for prediction are required for machine learning methods; however, faults of interest in US Army Ground Vehicle Maintenance Records appear as natural language text descriptions rather than a finite set of discrete labels.
 Transforming linguistically complex data into a set of prognostics classes is necessary for utilizing supervised machine learning approaches for prognostics. Manually labeling fault description instances is effective, but extremely time-consuming; thus, an automated approach to labelling is preferred. The approach described in this paper examines key aspects of the fault text relevant to enabling automatic labeling. A method was developed based on the hypothesis that a given fault description could be generalized into a category. This method uses various natural language processing (NLP) techniques and a priori knowledge of ground vehicle faults to assign classes to the maintenance fault descriptions.
 The core component of the method used in this paper is a Word2Vec word-embedding model. Word embeddings are used in conjunction with a token-oriented rule-based data structure for document classification. This methodology tags text with user-provided classes using a corpus of similar text fields as its training set. With classes of faults reliably assigned to a given description, supervised machine learning with these classes can be applied using related maintenance information that preceded the fault.
 This method was developed for labeling US Army Ground Vehicle Maintenance Records, but is general enough to be applied to any natural language data sets accompanied with a priori knowledge of its contents for consistent labeling. In addition to applications in machine learning, generated labels are also conducive to general summarization and case-by-case analysis of faults. The maintenance components of interest for this current application are alternators and gaskets, with future development directed towards determining the RUL of these components based on the labeled data.

Pagoda: A Hybrid Approach to Enable Efficient Real-Time Provenance Based Intrusion Detection in Big Data Environments

Efficient intrusion detection and analysis of the security landscape in big data environments present challenge for today's users. Intrusion behavior can be described by provenance graphs that record the dependency relationships between intrusion processes and the infected files. Existing intrusion detection methods typically analyze and identify the anomaly either in a single provenance path or the whole provenance graph, neither of which can achieve the benefit on both detection accuracy and detection time. We propose Pagoda, a hybrid approach that takes into account the anomaly degree of both a single provenance path and the whole provenance graph. It can identify intrusion quickly if a serious compromise has been found on one path, and can further improve the detection rate by considering the behavior representation in the whole provenance graph. Pagoda uses a persistent memory database to store provenance and aggregates multiple similar items into one provenance record to maximumly reduce unnecessary I/O during the detection analysis. In addition, it encodes duplicate items in the rule database and filters noise that does not contain intrusion information. The experimental results on a wide variety of real-world applications demonstrate its performance and efficiency.

Rule-based Data Quality Intelligent Monitoring System

The rapid development of Internet information technology brought great convenience to people’s lives and work, but it also brought the problem of information overload[l]. The data center is the middle layer that mediates the contradiction between the foreground and the background[2]. It provides a reusable, standardized, and agile multifunctional platform through the modeling of background data and the aggregation of data services to support the foreground for the demand[3] of quickly change according to market changes. Through the data center, we have opened up the data islands of each business system. However, when each business system uses data, it found that the data quality is poor, unstable, not timely, and inconsistent. Traditional cluster data quality monitoring has the characteristics of inconsistent data quality requirements for various services, difficult development, low processing efficiency, difficult maintenance, and insufficient intelligence.Based on this situation, we design a data quality monitoring system based on rules and artificial intelligence. General users perform simple configuration on the interface to complete real-time online monitoring of data, and real-time statistics on the number of data quality problems and a detailed list for users to trace the cause of the problem and identify abnormal scenarios in advance. The comparison between experiments and mainstream products proves that the system has greatly improved in terms of user use and flexibility.

Adaptation of a Cost Overrun Risk Prediction Model to the Type of Construction Facility

To assess the risk of project cost overrun, it is necessary to consider large amounts of symmetric and asymmetric data. This paper proposes a cost overrun risk prediction model, the structure of which is based on the fuzzy inference model of Mamdani. The model consists of numerous inputs and one output (multi-input-single-output (MISO)), based on processes running consecutively in three blocks (the fuzzy block, the interference block, and the block of sharpening the representative output value). The input variables of the model include the share of element costs in the building costs (SE), predicted changes in the number of works (WC), and expected changes in the unit price (PC). For the input variable SE, it is proposed to adjust the fuzzy set shapes to the type of building object. Single-family residential buildings, multi-family residential buildings, office buildings, highways, expressways, and sports fields were analyzed. The initial variable is the value of the risk of exceeding the costs of a given element of a construction investment project (R). In all, 27 rules were assumed in the interference block. Considering the possibility of applying sharpening methods in the cost overrun risk prediction model, the following defuzzification methods were investigated: the first of maxima, middle of maxima, and last of maxima method, the center of gravity method, and the bisector area method. Considering the advantages and disadvantages, the authors assumed that the correct and basic defuzzification method in the cost overrun risk prediction model was the center of gravity method. In order to check the correctness of the assumption made at the stage of designing the rule database, result diagrams were generated for the relationships between the variable (R) and the input variables of individual types of buildings. The results obtained confirm the correctness of the assumed assumptions and allow to consider the input variable (SE), adjusted individually to the model for each type of construction object, as crucial in the context of the impact on the output value of the output variable (R).

Design of 3D Model Database of Substation Equipment Based on Access Software

Building model base and sharing model effectively can accelerate the process of 3D new design and scene reconstruction, and reduce the cost of modeling has important engineering value. Based on the analysis of the functional requirements and architecture design of the three-dimensional model database of substation, this paper puts forward the storage information attributes and coding rules of the three-dimensional model database of substation typical equipment, and creates the three-dimensional model database of substation equipment based on access. The three-dimensional model database of substation includes model management and maintenance module, human-computer interaction module, which realizes the storage, storage, query and other functions of three-dimensional model of substation. The research results can provide reference for the construction of three-dimensional model base of substation equipment.

GENERATION OF TEST BASES OF RULES FOR THE ANALYSIS OF PRODUCTIVITY OF LOGICAL INFERENCE ENGINE

The subject of research in the article are test tasks to determine the performance of logical inference engines based on rules. The purpose of the work is to create a method of forming a database of rules and a set of data for analyzing the performance of logical inference mechanisms according to the given characteristics of rule activation and the complexity of finding a way to the target conclusion. The article solves the following tasks: determining the requirements for the knowledge base to be formed; creation of a knowledge base model; creating a way to form rules; identifying ways to increase the number of rules; providing testing of logical inference mechanisms for the proposed test problem. The following methods are used: methods of comparison with the sample, graph theory, logical programming. The following results were obtained: the method provides opportunities: creation of conditions of rules that complicate the data flow network of the Rete-algorithm as much as possible; formation of test bases of rules for derivation both on logic of the first order, and on offer logic; simply increase the number of knowledge base rules while maintaining the output logic. The formation of the knowledge base is based on a graph that represents the meta-rules of mixing paints to obtain a new colour. The vertices of the graph are colour classes. Each metarule is either an edge leading to the OR vertex or a set of edges in the case of the AND vertex. Each meta-rule specifies a scheme for creating several rules, because its structural components are classes of paints. The given structure of the graph significantly complicates the logical inference, because to prove the truth of the conclusion on AND-vertices it is necessary to have the conclusions obtained in the previous steps of different search directions. Examples of rule formation are given. Target vertices are defined, which determine the simplest and most complex cases of logical inference. Conclusions: it was proposed a semantic model of the knowledge base in the form of AND/OR-graph, which allows you to test the effectiveness of the implementation of conflict resolution strategies, as well as heuristic algorithms; a method of creating tests for inference mechanisms, which allows you to generate a database of rules and a set of data of certain sizes, as well as to model the complexity of finding the target output and activating the rules. Ways to increase the number of rules of the knowledge base to complicate the problem of logical inference have been presented; formulation of tests to determine the performance of logic output mechanisms for the proposed test problem has been done.

Modeling of a Catalytic Cracking in the Gasoline Production Installation with a Fuzzy Environment

The article offers a systematic approach to the method of developing mathematical models of a chemical-technological system (CTS) in conditions of deficit and fuzziness of initial information using available data of various types. Based on the results of research and processing of the collected quantitative and qualitative information, mathematical models of the reactor are constructed. Formalized and obtained mathematical statements of the control problem for choosing effective modes of operation of technological systems are based on mathematical modeling. Based on the obtained expert information, linguistic variables were described and a database of rules describing the operation of the input parameters of the reactor unit of the catalytic cracking unit was obtained.

Rules and validation processes for interoperable BIM data exchange

Abstract Diverse industries have recognized the significance of a neutral data standard for seamless building information modeling (BIM) data exchanges and adopted commonly agreed schemes such as the industry foundation classes (IFC) schema for enhancing BIM data interoperability. To selectively employ domain-specific data exchange requirements, the AEC-FM (the Architecture, Engineering, Construction, and Facility Management) industries have developed their own schema with a subset of the IFC schema, named a model view definition (MVD). However, because of latent human errors, inadequate MVD definitions, and error-prone data mapping problems, the adoption of IFC for exchanging and sharing BIM data is frequently limited with severe data integrity issues. This challenging situation requires the detailed examination of the limitations of the heterogeneous IFC translation processes of the current BIM authoring and application tools. To accomplish this objective, the authors thoroughly investigated the two MVDs, the Coordination View Version 2.0 and the Construction Operations Building Information Exchange, broadly used as an international standard for developing the IFC translation processes, and then identified their intrinsic requirements/rules and developed rule-based data validation processes. These new intrinsic knowledge of the two MVD specifications will be fundamental foundations to create coherent BIM data exchange systems that are currently scattered and dispersed in heterogeneous IFC translation processes and structures. Newly revealed rules pertaining to these two MVDs have been executed with IfcDoc, which is the IFC documentation tool. This BIM data validation process is expected to not only accurately evaluate the translation processes between BIM native data and IFC ones but also help industry professionals ensure the integrity and the accuracy of their BIM data according to the BIM data exchange standards using the two MVDs. In addition, the outcomes of this research study are expected to bolster the interoperable BIM adoption and offer the values of greater consistency of BIM data exchange.

Application of data mining technology in alarm analysis of communication network

Nowadays, with the continuous development of science and technology, the social demand for the network is more and more, and with the continuous expansion of the network scale, the complexity of the network is increasing day by day. How to locate the fault accurately and quickly from a large number of alarm information has become a problem. To solve this problem, we must use computer technology to study the automatic analysis technology of alarm correlation. Therefore, this paper applies data mining technology to communication network alarm analysis, and based on fuzzy theory, it can accurately describe the relationship between network alarm and fault reason, and combines data mining technology with fuzzy theory to form the network alarm correlation analysis method of fuzzy association rule mining. On this basis, in order to mine the fuzzy association rules of the fuzzy alarm database directly and avoid the interference in the process of converting the alarm database to the transaction database, this paper proposes a dynamic time window fuzzy association rule mining algorithm. Through the simulation analysis, compared with the traditional data mining technology, the fuzzy association rules mining method combined with the fuzzy theory has better performance, and the further proposed dynamic time window fuzzy association rules mining algorithm greatly improves the accuracy of the association rules confidence, and has good application performance in the network alarm correlation analysis.

A Rule-Based Data Quality Assessment System for Electronic Health Record Data.

Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners. We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring. We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information. The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set. This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.

Causes and consequences of reduced human intervention in formerly managed forests in Japan and other countries

In Japan, abandonment of forest management and underuse of forest resources are substantial challenges to environmental protection and sustainable wood production, while in many other countries, leaving a forest untouched may be perceived as positive for conserving the forest. This study thus analyses the scientific knowledge landscape on the causes and consequences of reduced human intervention in formerly managed forests and compares differences between Japan and other countries. Advanced search rules in the bibliographic databases returned 188 publications [35 for Japan and 153 for other countries] related to reduced human intervention in formerly managed forests. Reduced human intervention in formerly managed forests occurred in developed countries in the mid-twentieth century because of socio-economic changes and was primarily directed by small-scale private landowners, causing structural homogenisation, stand ageing, and canopy closure, often followed by the consequences of decreasing biodiversity and ecosystem services in Japan and other countries. In Japan, heavy dependency on wood imports at the expense of biodiversity and ecosystem services and the impending need for harvesting abundant mature forests due to the unbalanced tree age distribution possibly enhanced the recognition and urgency of underuse as a threat to biodiversity and wood security, while in other countries, multiple management objectives of diverse landowners probably dispersed the recognition of underuse. Although less recognised in other countries, the absence of forest management can pose a risk to biodiversity and ecosystem services. Hence, reactivating forest management in underused private forests can be a cost-effective precautionary approach against possible damage from underuse-induced ecosystem disservices. Policy implications for sustainable forest management in underused private forests are discussed from a perspective beyond conventional private ownership.

Proposal for Named Entities Recognition and Classification (NERC) and the Automatic Generation of Rules on Mexican News

In this paper we introduce a proposal for extracting facts from news on Mexican online newspapers through their RSS (Really Simple Syndication). This problem will be addressed by using the task of automatic named entities recognition and classification (NERC), as well as the semantic relation extraction among entities, so that we can build a database of facts and rules from the obtained entities in an automatic manner. The final aim is to be able to infer new rules through the use of the knowledge databases constructed and an inference engine. In order to build the NER model, we performa manual annotation of corpora with different tags that include the baseline tags (person names, organizations, locations, dates and numeral). The idea proposed is presented in this paper with an example scenario together with the procedure employed for solving the problem of automatic inference of new rules.

AIR SUPPLY REGULATOR FOR THE WORKING SPACE HEAT INSTALLATION BASED ON FUZZY LOGIC

The combustion process in the working space of a heat installation is simulated. The static characteristic of the dependence of fuel consumption on air consumption in the conditions of stabilization of heat generated as a result of the combustion process is shown, therefore, the possibility of optimizing this process is proved. A block diagram, mathematical model and algorithm for calculating energy-saving fuzzy control of the combustion process in the working space of a thermal installation are developed. Combustion Membership functions of fuzzy sets for input linguistic variables are compiled. The rule bases are formulated taking into account the static characteristics of the control object. Examples of technological and linguistic justification of fuzzy controller rules are given. The dynamics of changing the controlled parameter is determined by solving two differential equations. The search for levels of "cut-offs" for the prerequisites of each of the rules using the "minimum" operation in accordance with the compiled database of rules is performed. The defuzzification procedure was performed (bringing it to clarity). The advantages of this system over traditional stabilizing control systems under the action of a set of random factors and a significantly nonlinear relationship between input and output values are shown. Optimal values of scaling factors for the developed automatic control system are determined. Let's consider an example of the formation of a control effect when implementing fuzzy energy-saving control of the combustion process in the working space of the boiler unit No. 3 of the GKP-1A UGPU of Gazprom dobycha Urengoy LLC. The calculations of the amount of inleakage of atmospheric air, excess air flow, the sectional area of the pipe supplying air, the total air flow in the working space of the thermal installation, the volume of natural gas participating in the combustion process, heat resulting from the combustion, the amount of air that did not participate in the combustion process, the amount of heat required to increase the temperature of excess air until the average temperature of the flue gas remaining heat of the combustion reaction, changes in the oxygen concentration in the exhaust flue gases depending on the air flow to the burner. The functional dependence of the flue gas temperature on the natural gas consumption is constructed by approximating the initial data of the regime map of the steam boiler. The dynamic properties of an inertia-delayed controlled process are taken into account when using the Euler method. The combustion control mode influence on the specific fuel consumption is estimated. The effects of using a system with fuzzy control in terms of saving natural gas and electricity over the entire range of performance of a thermal installation are estimated.

Automatic weighted matching rectifying rule discovery for data repairing

Data repairing is a key problem in data cleaning which aims to uncover and rectify data errors. Traditional methods depend on data dependencies to check the existence of errors in data, but they fail to rectify the errors. To overcome this limitation, recent methods define repairing rules on which they depend to detect and fix errors. However, all existing data repairing rules are provided by experts which is an expensive task in time and effort. Besides, rule-based data repairing methods need an external verified data source or user verifications; otherwise they are incomplete where they can repair only a small number of errors. In this paper, we define weighted matching rectifying rules (WMRRs) based on similarity matching to capture more errors. We propose a novel algorithm to discover WMRRs automatically from dirty data in-hand. We also develop an automatic algorithm for rules inconsistency resolution. Additionally, based on WMRRs, we propose an automatic data repairing algorithm (WMRR-DR) which uncovers a large number of errors and rectifies them dependably. We experimentally verify our method on both real-life and synthetic data. The experimental results prove that our method can discover effective WMRRs from the dirty data in-hand, and perform dependable and full-automatic repairing based on the discovered WMRRs, with higher accuracy than the existing dependable methods.

Determining weight coefficients for an optimal system of control over electric energy generation in a combined electric power system

This paper reports a solution to a relevant scientific task of determining the weight coefficients for an optimal system of automated control over the level of electric energy generation by renewable energy sources in a combined electric power system. It has been established that existing control systems of electric power generation by renewable sources, based on solving a problem on multicriteria optimization, do not take into consideration a change in the quantitative and qualitative composition of electric consumers over time.It has been proposed, in order to determine those weight coefficients that influence the resulting solution to the problem on multicriterial optimization, to employ a mathematical apparatus of fuzzy logic. This has made it possible, in contrast to existing systems, to better account for a time-dependent change in the quantitative and qualitative composition of electricity consumers. It has been shown that it is expedient to choose, as the input parameters for a fuzzy model, the values for a coefficient of consumer load and a coefficient of load importance that takes into consideration the sensitivity of electric consumers to voltage deviations.A database of rules for a fuzzy system of weight coefficients determination has been constructed, which includes 15 rules of fuzzy production. The Mamdani algorithm has been used as an algorithm of fuzzy logic derivation. To determine the input and output linguistic variables we have used triangular and trapezoidal membership functions. The output parameter was defuzzified by the method of determining the center of gravity (centroid method).We have performed computer simulation of the optimal system of an automated system of control over the level of electric energy generation by renewable energy sources using the designed fuzzy unit for determining weight coefficients. Analysis of the simulation results has shown that the devised system of automated control makes it possible to increase the level of electricity generation to a network, as compared to existing systems

Discrete-time adaptive controller based on IF-THEN rules database for novel architecture of ABB IRB-1400

The robotic architecture based on variable-frequency drive (VFD) and databased equivalent model is utilized for improving the performance of the robotic arm ABB: IRB-1400. The multiplexer-buffer units of the robotic system are designed to select the output feedback for the controller when the input is frequency command for digital VFD units. The proposed robotic architecture is considered as a class of unknown nonlinear discrete-time systems when the robotic system is accessed only input-output data. The equivalent model is established by multi-input fuzzy rules emulated networks (MiFREN) with the IF-THEN rules database designated by human knowledge according to the robot’s tasks and input-output data. The adaptive controller is developed by the estimated pseudo-Jacobian matrix (PJM) when the closed-loop performance is guaranteed by the designed parameters. Furthermore, the experimental system is constructed to validate the performance of the robotic system and the proposed controller altogether with comparative results from existent schemes.

Robotic architecture as unknown discrete-time system based on variable-frequency drive and adaptive controller

Abstract The new architecture for outdated robotic arm (ABB: IRB-1400) is developed by the direct variable-frequency drive (VFD) and considered as a class of unknown nonlinear discrete-time systems. The identification model is established by Multi-input fuzzy rules emulated network (MiFREN) with the online learning algorithm and the database of IF-THEN rules. The controller is designed by using the identification model and feedback signals from the multiplexer when kinematic and dynamic models of the robotic system are neglected. The convergence of tracking errors and internal signals is guaranteed by both theoretical and practical aspects. The performance validation of the proposed robotic system is accomplished by the experimental setup with comparative results.