Will sophisticated computer systems replace professional librarians or complement their skills?

Low level industrial systems use simple data representation which reflects flat input and output structure of control industrial system. This structure is directly mapped to the flat memory allocation. The structural data organization is used in upper layers of industrial computer system. This article contains compare analysis of tag and object oriented data model used for vertical communication in distributed industrial computer systems. This analysis is based on classical tag approach used in the most of classical visualization systems and object oriented approach introduced in OPC UA architecture.KeywordsLayer DeviceManufacture Execution SystemObject Oriented ApproachIndustrial Control SystemObject Oriented DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The purpose of this tutorial presentation is to introduce G-Networks, or Gelenbe Networks, which are product form queueing networks which include normal or positive customers, as well as negative customers which destroy other customers, and triggers which displace other customers from one queue to another. We derive the balance equations for these models in the context of multiple customer classes, show the product form results, and exhibit the traffic equations which — in this case, contrary to BCMP and Jackson networks — are non-linear. This leads to interesting issues of existence and uniqueness of the steady-state solution. Gelenbe Network can be used to model large scale computer systems and networks in which signaling functions represented by negative customers and triggers are used to achieve flow and congestion control.

Problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from huge volumes of data. In this paper, the PMC diagnostic model is considered, based on the diagnostic approach of end-to-end probing technology. A probe is a test transaction whose outcome depends on some of the system's components; diagnosis is performed by selecting appropriate probes and analyzing the results. In the PMC model, every computer can execute a probe to test a dedicated system's components. Furthermore, any test result reported by a faulty probe station is unreliable and the test result reported by fault-free probe station is always correct. The aim of the diagnosis is to locate all faulty components in the system based on collection of the test results. A dual-cube DC(n) is an (n + 1)-regular spanning subgraph of a (2n + 1)-dimensional hypercube. It uses n-dimensional hypercubes as building blocks and returns the main desirable properties of the hypercube so that it is suitable as a topology for distributed systems. In this paper, we first show that the diagnosability of DC(n) is n + 1 and then show that adaptive diagnosis is possible using at most 22n+1 + n tests for a 22n+1-node distributed system modeled by dual-cubes DC(n) in which at most n + 1 processes are faulty. Furthermore, we propose an adaptive diagnostic algorithm for the DC(n) and show that it diagnoses the DC(n) in three testing rounds and at most 22n+1 + O(n3) tests, where each node is scheduled for at most one test in each round.

7 - Central site computer facilities

During the past ten years, a gap has developed between large digital computers and manual computing methods. This is particularly true with regard to the analyses and design of structural members. The increasing availability of large computer systems and their associated software, coupled with a dramatic reduction in cost, has placed computer analysis capability well within reach of most structural engineering offices. Member forces can be readily determined using either special frame analysis programs or general purpose programs which are available on commercial computer systems. However, once the member design forces are determined, the engineer must revert to hand calculations for determining and checking the design of the individual members. These calculations can become monotonous and time consuming for a structure of even modest size. Furthermore, such calculations tend to become inaccurate and insufficient. For example, a designer may accept a given section as adequate without trying other possibilities which may lead to a more efficient design. This paper has illustrated the use of a hand-held programmable calculator for the strength design of steel frames. Although the process is only semi-automatic, it still saves the designer from having to perform the rather mundane calculations involved with member selection and checking. Its main attraction is the low cost and ready availability of the calculators, plus the fact that the calculation can be done at the designers desk without extensive data preparation. The procedures assume that the design forces are known. Therefore, a force analysis will have to be done on a larger computer system. It should also be noted that the use of this type of design procedure allows complete interaction of the engineer with this design process. Therefore, engineering judgment decisions can be made and incorporated in the design as it progresses.

Expert-system technology has emerged from beneath the umbrella of Artificial Intelligence and into the mainstream of engineering problem solving. There already are several notable examples of the application of this state-of-the-art technology in the petroleum industry. Clearly, expert systems that run efficiently on standard personal computers are particularly of value. A new expert-system generator that overcomes the speed, memory and mass-storage limitations of XT/AT-class personal computers is presented in this paper. The shell is optimized for the specific characteristics of the target user, knowledge expert and computer. Expert systems developed using this shell currently are being used by our technical personnel for drilling fluid and related applications.

Today cyberattacks continue to evolve and are highly complex. They are also very expensive by the average cost of a breach-in cyberattack. The top ten most common cyberattack intrusion incidents for industrial, public, and private organizations are phishing attacks, negligent and malicious insiders, advanced persistent threats, zero day attacks, denial of service attacks, software vulnerabilities, social engineering attacks, and brute force attacks. Therefore, cybersecurity becomes an essential issue that generally focuses on the measures to protect valuable data, information, and business assets from malicious threat events that affect confidentiality, integrity, and availability of information. In this regard, it is vitally important that computer systems, networks and network-connected devices, infrastructure resources, and others stay up-to-date with current software operating systems, patches, and releases. Therefore, organizations need to institute policies and procedures that enforce the way their user’s access information and interact with network or system resources. Here the NIST Cybersecurity Framework and the MITRE Cybersecurity Criteria come into play. The NIST Cybersecurity Framework is a set of best practices, standards, and recommendations that support organizations to improve their cybersecurity measures. It focusses on using business drivers to guide cybersecurity activities and considering cybersecurity risks as part of the organizations cybersecurity risk management. In this regard, the framework provides a common organizing structure for multiple cybersecurity approaches by assembling standards, guidelines, and practices that are working effectively today. The MITRE Cybersecurity Criteria enable a collective response against cybersecurity threat events, worked out in conjunction with industry and government authorities. It describes the common tactics, techniques, and procedures of advanced persistent threats against organizations’ computer systems and networks and was later expanded to industrial control systems. In this regard, the MITRE Cybersecurity Criteria are fully committed to defending and securing cyber-ecosystems. NIST’s and MITRE’s goal is to develop cyber resiliency approaches and controls to mitigate malicious cyberattacks. Cyber resiliency enables anticipating, withstanding, recovering from and adapting to adverse conditions, stresses, cyberattacks, or compromises on computer systems, networks, infrastructure resources, and others. Against this background, this chapter introduces in Sect. 5.1 the NIST Cybersecurity Framework (NIST CSF) with their manifold possible uses and their great impact improving industrial, public, and private organizations’ cybersecurity needs. Therefore, Sect. 5.1 introduces the process of cybersecurity risk management. Since NIST CSF is one of the most relevant cybersecurity frameworks, Sect. 5.1 introduces the NIST Cybersecurity Framework. Section 5.1.1 introduces CIS Critical Security Controls, Sect. 5.1.2 ISA/IEC 62443 Cybersecurity Standard, Sect. 5.1.3 MITRE Adversarial Tactics, Techniques, and Common Knowledge, Sect. 5.1.4 NIST 800-653, and in Sect. 5.1.5, the NIST Cybersecurity Framework. Section 5.2 focuses on the NIST Cybersecurity Framework for Critical Infrastructure and focuses in Sect. 5.2.1 on a NIST CSF Critical Infrastructure best practice use case, making use of a model approach in cybersecurity maturity. Against this background, Sect. 5.3 focusses on the MITRE Cybersecurity Criteria that provides a common taxonomy of Tactics, Techniques, and Procedures, applicable to defend cyberattacks, to withstand cyberattackers activities like unauthorized interaction with organizations’ computer systems, networks, and infrastructure resources, to recover from potential malicious cyberattacks. Section 5.4 introduce the MITRE Cybersecurity Taxonomy, which refers to cyberattack possibilities and how to conquer them. Section 5.5 contains comprehensive questions on the topics of NIST Cybersecurity Framework and MITRE Cybersecurity Criteria. Finally, “References” refers to the used references for further reading.

1982 Canadian conference on industrial computer systems

Building large resilient and computer systems can be challenging, especially when temporal predictability is also required. Large resilient systems are typically composed of different networking technologies, for example, using different wired and wireless communication technologies. Building such systems with safety-relevant services demands appropriate system interfaces and algorithmic concepts. In this talk we present some design patterns that are useful to build such large resilient and predictable systems. Often, the underlying communication medium does not provide the reliability and real-time properties that are needed for a concrete application. In such a case the presented design patterns can be implemented as a communication layer on top of the underlying communication system. We discuss design patterns for interaction autonomy, system communication and reliability.

The K-Rep system was built to explore the utility of a KL-One style knowledge representation in the development of expert systems. Beginning in about 1985, our activity in expert systems has been centered on the FAME (FinAncial Marketing Expertise) system[4]. FAME attempts to provide support to an IBM marketing representative in the financing decisions involved in the acquisition of large mainframe computer systems. Based on our experience in building a feasibility demonstration of FAME using a rule based approach, we concluded that the rule based technology would not easily scale up. Thus we abandoned the rule based approach in favor of organizing the system as a set of problem solvers around a common conceptual core. Since diverse problem solvers would be utilized in FAME, it was thought desireable that the conceptual core have a well-defined, enforceable semantics. These considerations led us to the KL-One[3] style knowledge representation.

Biofilms are considered to be ubiquitous in industrial and drinking water distribution systems. Biofilms are a major source of contribution to biofouling in industrial water systems. The problem has wide ranging effects, causing damage to materials, production losses and affecting the quality of the product. The problem of biofouling is operationally defined as biofilm development that exceeds a given threshold of interference. It is for the plant operators to keep biofilm development below the threshold of interference for effective production and to work out values for threshold limits for each of the technical systems. Industrial biofilms are quite diverse and knowledge gained with a certain type of biofilm may not be applicable to others. In recognition of this, the old concept of a universal/effective biocide is a misnomer as physical, chemical and biological parameters of source water vary from site to site and so do the interactions of biocides with these parameters. Control methods have to be tailor-made for a given technical system and cannot be extrapolated. Because of the wide-ranging complexity in industrial technical systems, understanding the biofilm processes, detection, monitoring, control and management is imperative for efficient plant operation. A successful antifouling strategy involves prevention (disinfecting regularly, not allowing a biofilm to develop beyond a given threshold), killing of organisms and cleaning of surfaces. Killing of organisms does not essentially imply cleaning as most industrial systems deploy only biocides for killing, and the cleaning process is not achieved. Cleaning is essential as dead biomass on surfaces provide a suitable surface and nutrient source for subsequent attachment of organisms. A first step in a biofilm control programme is detection and assessment of various biofilm components, like thickness of slime layer, algal and bacterial species involved, extent of extracellular polymeric substances and inorganic components. Prior to adopting a biocidal dose and regime in an industrial system, laboratory testing of biocides using side-stream monitoring devices, under dynamic conditions, should be carried out to check their effectiveness. Online monitoring strategies should be adopted and biocidal dosing fine-tuned to keep biofilms under control. Literature on biofilm control strategies in technical systems is rich; however, the choice of the control method often depends on cost, time constraints and the cleanliness (threshold levels) required for a technical process. Currently, there is a trend to use strong oxidizing biocides like chlorine dioxide in cooling systems and ozone in water distribution systems as low levels of chlorine have been found to be ineffective against biofilms. A number of non-oxidizing biocides are available, which are effective but the long-term effects on the environment are still unclear. New techniques for biofilm control like ultrasound, electrical fields, hydrolysis of extracellular polymeric substances and methods altering biofilm adhesion and cohesion are still in their infancy at the laboratory level and are yet to be successfully demonstrated in large industrial systems.

The need for detection, identification and prediction of anomalies emerged just a short time ago. This task brings multiple challenges that are mainly associated with recording and detection of novel attacks and influences. It draws attention of experts in network security and diagnostics of information and industrial systems. Detection of anomalies in dynamic dataflows largely determines the efficiency of management of computer network information security within information and industrial systems. At the same time, the technology of dynamic dataflow prediction is also very important for building systems intended to detect anomalies in data protection within industrial control systems. Any automated systems are based on available computer capabilities and advances in management theory, mathematical modeling and optimizations methods. Processes that occur in industrial automated systems and that are reflected in dataflows being observed constitute complex multi-component processes, thus making it more complicated to predict such processes. In this case, complex multi-component time series data should be predicted at different (short-term, medium-term and long-term) time scales. A neural network architecture matching the structure of the multi-component time series being predicted should be built for generating a multi-component prediction. It is proposed to decompose the complex multi-component time series into several basic components using the digital signal processing technology, i. e. to perform a preliminary structural analysis of multi-component time series within the observed range of all time series that reflect operation of the industrial control system. Separate predictions with different time horizon are formed for each basic component of the multi-component time series using the available neural network architecture and machine learning taking into account dynamic characteristics of the above components. Anomalies in the observed range of multiple time series that reflect operation of the industrial control system are detected (identified) through component-wise comparison of each component (resulted from the above preliminary digital processing) of any time series within the observed range of all time series, with each prediction of the relevant component of the above time series within the observed range of all time series. This approach that implies component-wise comparison will allow to detect anomalies within the range of observed time series of the industrial control system separately by their different dynamic characteristics, and thus will improve the efficiency of management of information security within information and industrial systems.

Modern computer networks generate significant volume of behavioural system logs on a daily basis. Such networks comprise many computers with Internet connectivity, and many users who access the Web and utilise Cloud services make use of numerous devices connected to the network on an ad-hoc basis. Measuring the risk of cyber attacks and identifying the most recent modus-operandi of cyber criminals on large computer networks can be difficult due to the wide range of services and applications running within the network, the multiple vulnerabilities associated with each application, the severity associated with each vulnerability, and the ever-changing attack vector of cyber criminals. In this paper we propose a framework to represent these features, enabling real-time network enumeration and traffic analysis to be carried out, in order to produce quantified measures of risk at specific points in time. We validate the approach using data from a University network, with a data collection consisting of 462,787 instances representing threats measured over a 144 hour period. Our analysis can be generalised to a variety of other contexts.

Small and medium enterprises (SMEs) act as a useful vehicle for economic growth because they have the capacity to achieve rapid economic growth while generating considerable employment opportunities. However, SMEs are heavily criticized due to their high failure rate. It is important to study the factors that contribute to the success of entrepreneurs, so they can be emulated. As highlighted in many studies, lack of managerial knowledge and skills hamper the rapid growth of the SME sector. This research attempts to investigate the impact of managerial and entrepreneurial skills on the success of SMEs in the North Western Province in Sri Lanka. Simple random sampling was employed to select 129 SMEs as the sample. A questionnaire was distributed among owner–managers of these businesses to collect data. Conceptual, human and technical skills were considered as managerial skills, and entrepreneurial skills were also analyzed. Owners’ satisfaction with the financial and non-financial success of the enterprise was selected to measure business success. The study revealed that technical and entrepreneurial skills have a significant positive impact on business success. The findings can help policymakers who are connected to entrepreneurship development. They can organize programs to develop skills identified by this research that positively impact business success. The growth of SMEs can be accelerated by developing owners’ managerial knowledge and identified skills.

The complexity of large computer systems has raised unprecedented challenges for system management. In practice, operators often collect large volume of monitoring data from system components and set up many rules to check data and trigger alerts. However, the alerts from various rules usually have different problem reporting accuracy because their thresholds are often manually set based on operators' experience and intuition. Meantime, due to system dependencies, a single problem may trigger many alerts at the same time in large systems and the critical question is which alert should be analyzed first in the following problem determination process. In this paper, we propose a novel peer review mechanism to rank the importance of alerts and the top ranked alerts are more likely to be true positives. After comparing a metric value against its threshold to generate alerts, we also compare the value with the equivalent thresholds from many other rules to determine the importance of alerts. Our approach is evaluated with a real test bed system and experimental results are also included to demonstrate its effectiveness.