Topic 5. KM Strategy.
Question 1. State and justify some tools, technologies, processes, and/or methods that could be used to support personalisation and/or codification strategies.
A knowledge management strategy includes two key groups of components: codification components and personalization components. The former components relate to storing, codifying and retrieving knowledge and are largely technology related (Jashapara, 2004). Codification tools and technologies deal with explicit knowledge and are powered by database engines performing operations with information. It should be noted that codification components are largely standardized and rely on “tried and true” methods and approaches. Personalization components focus on people rather than on technology and aim to develop people. Personalization processes deal mostly with tacit knowledge, encourage dialogue, creativity and innovation (Jashapara, 2004).
With regard to codification, the most popular technologies are intranets/extranets, web-based learning or training software (or services), data mining tools and document management systems (or content management systems) (Schwartz, 2006). The use of intranets/extranets allows to store knowledge on the server, share it in an effortless way, provide access to users both inside and outside the organization (Rao, 2012). Web-based training systems are frequently used for knowledge codification since these systems can be used to share existing knowledge with people quickly and efficiently. For example, large companies use edX online platform to develop corporate courses (and therefore, codify and manage their knowledge).
Data mining is the process of identifying patterns in large sets of data. One of frequently used software packages is STATISTICA. This software can perform text mining, analyse enterprise performance, enhance decision-making and optimize processes (Rao, 2012). Hence, this software allows to manage explicit knowledge, create and codify new knowledge. Content management systems efficiently organize explicit information.
With regard to personalization, the most commonly used processes are mentoring, organizing working groups, videoconferencing, etc (Schwartz, 2006). Mentoring enables the transfer of tacit knowledge and experience from more advanced workers to freshers. According to Schwartz (2006), teleconferencing and videoconferencing are the most popular methods of sharing tacit knowledge; moreover, these processes are most frequently used when people need to find some help or advice. Another powerful approach is the use of working groups – people use these groups to connect with each other and generate new tacit knowledge.
Question 2. Collaborative Space – Unilever Case Study q1-3. (p. 113)
1) What advice would you give Cathy Bautista on improving the strategic focus of Unilever’s knowledge management strategies?
It seems that Unilever’s approach to knowledge management is based on the “typical” knowledge management activities such as generating new knowledge, codifying existing knowledge and stories, etc. However, Unilever should consider developing a knowledge strategy basing on the company’s needs instead of planned activities. Such approach will lead to a more efficient knowledge management system that would address key knowledge management problems at Unilever.
2) What changes, if any, would you make to Unilever’s communities of practice?
Two areas that seems not covered by the communities of practice (CoPs) as they are implemented at Unilever include applied meaning of knowledge generated by CoPs and knowledge sharing with those employees who are not part of particular CoP. The proposed changes are as follows. It would be useful to empower group activists to establish contact with the company’s divisions that might benefit most from the group’s achievements, to ensure that the relevant divisions can access the knowledge and apply it. In addition, group activists could facilitate the process of translating CoP’s knowledge into practice.
Another issue of CoPs is information sharing. Although it is evident that CoPs generate a lot of documentation, it would be difficult for an external person to navigate these archives and find the necessary information. So, it is recommended to create a convenient navigation system and competent mentors/tutors for each particular CoP.
3) How could “learning histories” be further developed to capture organisational memory?
It will be very useful to keep the questions and answers appearing in “Mastermind” to create a knowledge database that would form the basis for an expert knowledge database. Furthermore, experts could also write down the questions and answers they believe to be most important in their knowledge domain, therefore expanding this expert knowledge database. Learning histories can be further expanded into best practices and guidelines that might be very helpful for freshers. It might be efficient to create a corporate wiki for experts and let them collaborate while sharing their valuable knowledge.
Question 3. At home or in the lab download the Decision Explorer software (eg from BlackBoard) and enter your version of the cognitive map from the lab. “How can we improve safety for airline passengers?” Include some screen captures showing your maps.
- What are the main features of cognitive mapping?
Cognitive maps represent specific domains, their analysis and interrelationships between different domain parts or concepts using visual representation. Cognitive maps help to structure complex domains and situations, allow to grasp an overview of a specific problem or situation. The use of visual representation helps to focus on solutions rather than problems and allows to compare different alternatives (Rao, 2012). Cognitive maps are efficient for identifying patterns, issues, viewpoints and other specifics of information pertaining to the considered situation. Furthermore, cognitive maps can be used for structuring information, formulating arguments and achieving consensus (Jashapara, 2004).
- How could it be used to support strategy development?
Cognitive maps help to assess complex situations and domains, and therefore they can be invaluable for strategy development. Cognitive mapping connects tacit and explicit knowledge, and helps establish links between information originating from these types of knowledge. One way of using cognitive maps for strategy development is called SODA – Strategic Options Diagnostic Analysis (Rao, 2012). This is a causal mapping approach which reconstructs the opinions and visions of different stakeholders; individual cognitive maps are then connected into a large map and causal relationships between concepts are identified. Such approach allows to prioritize between the needs of different stakeholders and outline the most important strategic directions for future development.
- What analysis options does the DE software provide?
DE software includes such analysis options as mapping a concept, exploring a concept, identifying explanations and consequences. DE software also provides a list of differen analyses: central, cluster, collapse, cotail, domain, focus, hieset, loop and potency. DE software offers to set mapping options (which include hierarchical and tree mapping) and clustering options for analysis purposes. The examples of cognitive maps for improving safety for airline passengers created in DE software are shown on Fig. 1 and Fig. 2 accordingly.
Figure 1. General cognitive map for improving airline safety
Figure 2. More detailed cognitive map for improving airline safety
Topic 6. KM and Culture
Question 1.How can competitive and collaborative cultures respectively affect KM strategies? Illustrate with reference to Orlikowski’s articles (see BlackBoard and/or e-reserve Library for the articles).
Organizational cultures can be classified according to various factors; one common classification is the division into cooperative and competitive cultures. These cultures are not precise; instead, it is possible to view these two types of organizational culture as two scale extremes, between which there is a continuum of cultures with different degrees of individualism/collectivism. Competitive cultures are more focused on individual values, goals, achievements and knowledge. In such organizations, a lot of attention is paid to achieving targets, and such qualities as aggressiveness and initiative are praised. In cooperative cultures, more attention is paid to building relationships and linkages. In such organizations, teamwork is highly important, and such qualities as being a good team player, sharing and creating synergy are very valued.
Organizational culture and the degree of cooperativeness or competitiveness embedded in the culture strongly influence the development and implementation of KM strategies. In competitive organizations, the focus is on knowledge codification strategies and on explicit knowledge. In cooperative organizations, more attention is paid to tacit knowledge and to sharing this knowledge using mentoring, working groups and other forms of team working and teaching. Furthermore, the use of knowledge management tools and technologies is strongly shaped by organizational culture. According to Orlikowsky (1993), the adoption of groupware technology such as Lotus Notes is different in cooperative and competitive cultures. While cooperative cultures value sharing and adopt groupware technologies more willingly, it is difficult to implement groupware in competitive cultures. The use of groupware in competitive cultures is focused on achieving individual aims and does little to foster collaboration if there are few incentives for sharing in the organization (Orlikowsky, 1996). Therefore, organizations should align knowledge management strategies with cultural dimensions.
Question 2. How could the OCI (Organisational Culture Inventory) circumplex (Jashapara, 2011, p. 276) be used in changing between an aggressive/defensive culture and a constructive culture? Illustrate with a diagram. What management strategies/tactics might be required to make the change?
A well-researched and efficient instrument of analysing organizational culture and its dimensions is Organizational Culture Inventory (OCI). OCI is developed basing on two constructs shaping culture – concern for people/tasks and behaviour driven by the security of self-actualization (Jashapara, 2004). Each of these constructs can be aligned with certain organizational norms which in their turn can be matched to the types of organizational culture. OCI deals with three types of organizational culture – constructive, passive/defensive and aggressive/defensive (Jashapara, 2004). Constructive cultures encourage team working, communication and collaboration. Passive/defensive cultures are characterized by people’s expectations to interact with others in such a way that own security can be protected. Aggressive/defensive cultures are focused on tasks rather than on people, and therefore members of such cultures act to protect their needs and status.
It is possible to assess the organization’s culture using the dimensions provided by OCI, considering culture-shaping behaviours and identifying the relevant norms. Furthermore, OCI shows the relationships between different organizational norms and can be used for planning a transition from one cultural dimension into another. For example, if OCI shows that the organization currently has an aggressive/defensive culture, it is possible to plan a strategy towards building a constructive culture using the OCI diagram (Fig. 3).
Figure 3. OCI diagram (Jashapara, 2004)
Aggressive/defensive culture is formed by power norms or competitive norms from the “concern for people/tasks” dimension and by oppositional norms or perfectionist norms from the “behaviour driven by self-actualization of security needs” dimension (Jashapara, 2004). Constructive cultures, in their turn, are shaped by self-actualizing or humanistic-encouraging norms from the people/task dimension and by achievement or affiliative norms from the self-actualization/security dimension. Therefore, to initiate transition from aggressive/defensive culture to constructive culture, an organization should, for example, identify competitive norms and introduce self-actualizing norms and practices instead, and pay attention to shifting the focus from perfectionist approaches to achievements.
Question 3. Collaborative Space – Fluor Case (p. 291) q1-3.
- What changes to Knowledge OnLine should John McQuary recommend to Alan Boeckmann in light of Fluor’s rapid expansion in South America?
The foremost issue is the reliance on the English language; since there will be many new users who do not speak English, the majority of knowledge might become inaccessible to them. It would be useful to set up automatic translation for minor discussions (such as brief post comments, etc.) and hire translators to create Spanish versions of important posts, documents, etc. It is also recommended to use more personal methods of sharing knowledge such as workgroups, videoconferences, etc. These methods are more efficient for sharing tacit knowledge and encourage the development of cooperativeness within the organization.
- What are the shortcomings of Fluor’s online communities and how could they be improved?
There seems to be little collaboration between the communities; there are business and functional types of the communities, but it is not clear how Fluor communities handle questions that are on the edge of business and functional proficiency. Therefore, it is recommended to introduce a mechanism for implementing community interactions; in particular, it might be useful to create meta-groups for communities and research questions involving experts from different fields. In addition, it is not clear whether the achievements of important communications and discussions are further used in the company or whether they are simply kept in the archives. It is recommended to perform analysis of community achievements and to organize conferences or video meetings representing the results and updates.
- How can Fluor get employees to share project mistakes on Knowledge OnLine for the benefit of other employees and the organization?
First of all, Fluor can introduce awards for those who learn the most by their mistakes and who have achieved a lot due to analysing their mistakes. Introducing such a nomination in the annual KM success recognition. Secondly, it seems that the culture of Fluor is still more competitive than collaborative since the company had to introduce additional incentives to encourage knowledge and experience sharing. Therefore, it might be useful to give the opportunity to share project mistakes and failures anonymously, so that people would be encouraged to share their failures without risking their reputation. In addition, it would be useful to prepare documentation showing how certain failures later evolved into best practices and created benefits for the company, so that people would view opportunities in failures rather than threats.
Question 4. What are the strengths and weaknesses of a groupware like GroupSystems ThinkTank that we explored in the laboratory?
Groupware tools represent software that provides options for people to collaborate on projects using intranets or the web (Jashapara, 2004). Groupware is also referred to as collaborative software. This type of software is especially popular for remote collaboration or for collaborating in large companies where people are located far from each other. Groupware has both advantages and disadvantages. Key advantages of groupware are the following (eCollaborating, 2014).
- Groupware creates a structured and organized environment for remote communications and collaborative activities.
- Groupware fosters innovation and creativity since team members are able to introduce and store different ideas; in addition, most groupware products offer various options for storing ideas, brainstorming, branching ideas, etc.
- Groupware encourages communication and interaction between team members.
- Due to groupware, the connectivity in the team increases due to greater availability of team members – they can take part in team discussions from any place.
- Groupware also encourages continuous discussion and cooperation: team members can initiate discussions, ask questions and post updates whenever they feel like it. As a result, collaboration and progress become continuous.
- Groupware provides options for voting, real-time analysis, provides various templates and solves many routine organizational functions.
At the same time, it is possible to name the following disadvantages of groupware (eCollaborating, 2014).
- Groupware might require additional actions and consume more time; for example, asking questions and sharing expertise is faster in person than using remote communications.
- If groupware fails, collaboration will be disrupted.
- Groupware products with rich functionality are costly, and, furthermore, the choice of one groupware product might tie the whole team or organization to a specific vendor and make people dependent on vendor’s changes.
- Groupware creates additional security issues.
- There is no opportunity for non-verbal communications in groupware (although videoconferencing might partly resolve this problem).
Topic 7. KM and Business Intelligence and Big Data.
Question 1. Define Business Intelligence. Define Big Data.
There are many definitions of business intelligence. One of them is the following: business intelligence is an activity, a process or a tool that is used for getting the best information for the decision-making process (Waltz, 2003). The purpose of business intelligence is therefore supporting decision-making through accessing, transforming and analysing business data. Due to its broad nature, business intelligence includes a variety of tools and processes such as performance management, data warehouses, data mining, business analytics, efficient user interface, etc. (Jashapara, 2004). Tools for statistical analysis, financial analysis, reporting, forecasting, querying, performing multidimensional analysis, data visualization tools – all these instruments are used for gaining business intelligence.
A lot of attention is currently paid to big data and their analysis. Big data is the term used to denote a data set or a collection of data set that are too complex and large to be handled by traditional data processing software (Payne & Embi, 2014). Key characteristics of big data are volume, velocity (the speed of data generation) and variability (semantic or syntactic data complexity) (Payne & Embi, 2014). If a resource or a data set has one of more of the above-mentioned characteristics, it can be named big data.
Question 2. Explore the Curtin HIVE online. What features does it provide to support visualisation? How is visualisation important to Business Intelligence and Big Data?
Curtin HIVE is the Hub for Immersive Visualization for eResearch. This initiative represents a multidisciplinary facility with four huge displays that can be used for various kinds of data visualization. The facility is powered by kinetic equipment and provides a variety of opportunities for research, data visualization, videoconferencing, collaborating, showing presentations, modelling, etc. The facility has 3D functionality and users can view three-dimensional videos and environments after putting on 3D glasses.
The features for data visualization offered by HIVE include four large screens (visualization systems) empowered by high-end computer systems and advanced video and graphics processing cards. One visualization system is a tiled display consisting of high-resolution screens (LCD panels); another visualization system allows to show stereoscopic images and panoramas using a surface for cylindrical projection. One more visualization system is called the Wedge; it represents two large displays that can be mounted either in a 90-degree angle to each other or next to each other as a flat screen. This system can demonstrate 3D content and is very efficient for displaying volume visualizations, three-dimensional environments and videos. Fourth visualization system is the dome of HIVE; it is a 360-degree panorama mounded in the dome of the facility. This system can be used for demonstrating virtual worlds, real-world panoramas and virtual environments.
Data visualization is highly important for creating business intelligence since it helps to represent data in a clear and concise way, allows to identify data patterns, tendencies and helps to match existing data to performance goals and target indicators. Furthermore, data visualization is especially important for analysing big data due to the complexity and diversity of big data. Data visualization is widely used to enhance decision-making and is therefore one of key tools for developing business intelligence.
Question 3. Collaborative Space – How could Business Intelligence and Big Data be applied to an international company like Toyota? What would be the business drivers? What would be the major challenges to successful implementation?
Large international companies require business intelligence solutions due to the varied nature of business data and the complexity of decision-making in such companies. An example of a company that will notably benefit from implementing business intelligence solutions is Toyota. Key business intelligence drivers in the case of Toyota will be achieving business excellence and adhering with Six Sigma standards, optimizing costs and processes, increasing competitiveness, improving the quality of reporting to key stakeholders, introducing more precise data reporting (on weekly or even daily basis), tracking additional business parameters in order to enhance decision-making.
Potential challenges for implementing business intelligence solutions in Toyota are the presence of strong hierarchy in Toyota’s organizational culture (hierarchy is often linked to data sharing limitations which are hardly compatible with generating business intelligence), the choice of improper business intelligence tools and solutions, the lack of professionals in business intelligence and the issues related to implementing new data sharing and data analysis practices.
An example of business intelligence implementation in Toyota took place when the company faced the increase of costs while supplying cars to dealers across the United States. Information was scattered and was stored in different types and formats; Toyota implemented a data warehouse solution and started recording historical data along with performing data analysis (Business Intelligence Articles, 2011). However, the system did not have proper functionality and was abandoned by managers. After a new business intelligence system with real-time data collection, management and reporting was installed, Toyota’s performance and profits notably increased (Business Intelligence Articles, 2011).
Question 4. As learning goes more online, learning analytics is becoming an important area. How could Curtin University use learning analytics to improve its teaching and learning? What issues would it face?
Learning analytics is the use of data produced by learners, intelligent data and various analytic models for the purpose of discovering learning patterns, social connections and for identifying new ways and approaches to learning (Chang & Li, 2014). Curtin University could use learning analytics by collecting the data about learners and their activities such as number of visits, data spent on learning particular modules, learning choices, requests and results. It is recommended to collect these data in real time and to trace learning dynamics along with learning patterns.
In order to improve learning and teaching online in Curtin University with learning analytics, it is recommended to do the following. The data obtained in the process of learning analytics should be used to reflect the achievements of learners, their personal progress and patterns of learning behaviour. The system might provide feedback to learners and give recommendations on improving learning results. In addition, the system could rely on spaced review techniques to recommend students to refresh their knowledge. Furthermore, data provided by learning analytics can be used to create personalized learning courses consisting of modules selected to match the learning preferences and abilities of an individual student.
Learning analytics results can also be used for identifying students who need extra attention and support, and can be used to trace gifted learners (and to offer more learning challenges to them). Furthermore, analytics might assist teachers and support personnel in planning interventions, scheduling and addressing students’ learning needs. Learning groups and administrators can also benefit from learning analytics since the results of analysis can be used for enhancing existing courses, expanding courses and developing new courses.
Potential challenges to using learning analytics and implementing changes basing on its results are (Chang & Li, 2014): creating the connections between the results of analysis and learning improvements, developing optimal approaches to analysing learning data, ensuring that learning analytics complies with ethical standards, securing private data from public disclosure.
Topic 8. Expert Systems (part one).
Question 1. How are expert systems and ontologies similar/different?
There are different definitions of expert systems and ontologies. An expert system can be defined as a system that uses codified human knowledge for solving problems that previously had to be solved by a group of people. It can be stated that expert systems model or imitate the process of reasoning used by a group of experts and model the decisions made by a group of experts (Hrebicek, Schimak & Denzer, 2011). The advantage of expert systems is the ability of these systems to propagate specific and/or rare expert knowledge and make expert decision-making available to a wide number of people and organizations. An ontology (in the sense of knowledge management discipline) is defined as a conceptualization of a specific domain or a field of knowledge that cannot be represented as an hierarchy (Jashapara, 2004). Ontology can also be viewed as a scheme or a knowledge map.
There are both similarities and differences between the notion of expert system and ontology. Both are used for representing knowledge pertaining to a particular domain, and both can be applied to performing operations with knowledge – storing, sorting, searching, etc. However, there also exist several differences. An ontology is a mechanism for conceptualizing knowledge and introducing specifications for this knowledge (Hrebicek, Schimak & Denzer, 2011). In other words, an ontology can be perceived as a vocabulary for a specific domain. An expert system contains knowledge and specific rules used by experts for decision-making (Hrebicek, Schimak & Denzer, 2011), so an expert system represents not only the structure of knowledge, but also the units of knowledge, relationships between them, weights of particular knowledge or facts and rules for decision-making.
Question 2. Explain how your assignment ontology could become an expert system.
An expert system commonly consists of two distinct parts, a shell and a knowledge base (Wolff et al., 2011). The shell deals with the structure of the expert system, its operations, vocabulary and underlying principles. while the knowledge base contains a collection of knowledge collected from experts and placed into the system using the principles outlined by the shell (Wolff et al., 2011). Expert systems are characterized by the high level of competence of their knowledge base and the usefulness of this base for decision-making in a particular domain.
An ontology can be viewed as a shell when the domain knowledge base is represented in a verbal form. Therefore, it is possible to use the assignment ontology to create an expert system. In order to develop the ontology into an expert system, the following steps are needed. First of all, domain ontology should be built and it is necessary to ensure that this ontology effectively represents key knowledge concepts and vocabulary. Then it is possible to develop a shell that interprets the ontology and its elements (Wolff et al., 2011), and to populate the system with expert knowledge using the ontology-empowered shell. Although building such customized expert systems built over an ontology-derived shell is complex and often time-consuming, such systems can efficiently represent knowledge and store expert knowledge in a format suitable for decision-making.
Question 3. Collaborative Space – The following is a demo of a dog breed selection system. http://www.exsys.com/Demos/Dogs/DogTitle.html What are the strengths and limitations of such a system?
The considered dog selection system represents a model of a limited expert system focused on identifying the optimal dog breed basing on a variety of criteria. This system is easy to use – its purpose is clear and the process of using the system is straightforward. The system has many advantages:
- it can be used by a person who knows almost nothing about dogs and taking care of them;
- the process of selecting a dog is divided into steps and each step is grouped around several characteristics;
- the system helps the future dog owner to understand what are the key factors and challenges related to dogs;
- the system offers several breeds and assigns scores to each of them basing on the user’s criteria;
- the pictures of breeds are shown and the specifics of each breed are described.
At the same time, this system does have a number of limitations:
- it relies on a predefined set of factors whereas the potential dog owner might be concerned about some specific factors not included into the list;
- the questions have a limited number of answers and not all potential options are included (for example, in the question related to typical temperatures the option of both very hot and very cold temperatures simultaneously is not included);
- it is not clear how the scores are derived and how the components of scores are calculated;
- the information about the breeds is clearly aligned to the factors that were used to develop questions but it is hard to determine the extent to which a particular breed corresponds to the requirements based on a particular factor (visual representation would be better in this case).
Topic 9. Expert Systems (part two).
Question 1. What is the difference between backward and forward chaining? Give a small example of each. Why is it of any importance?
Expert systems are computer systems that accumulate knowledge of human experts and are intended to emulate decision-making functions of human experts. Most expert systems use bases of knowledge obtained from human experts and then codified; decisions in such systems are made using specific rules of reasoning. The approach to decision-making in expert systems is highly important because different methods are needed for different types of tasks (Sauter, 2014). It is possible to classify reasoning rules into two types – backward chaining and forward chaining rules (Sauter, 2014).
The process of backward reasoning is based on selecting a goal and working backwards from it. The process of reasoning in systems using backward chaining starts with selecting a hypothesis or a set of goals and then going backwards to check whether the premises (antecedents) leading to the target goal or conclusion are true (Kendal & Creen, 2007). For example, it is possible to consider a system for detecting whether the selected animal is a horse or a cattle representative. Assuming that there are 2 rules: “If the animal has hooves and dewlaps, it is a representative of cattle”, “If the animal has no hooves and no dewlaps, it is a horse” and there are the following facts – “The animal has no hooves”, “The animal has no dewlaps”, then the process of backward reasoning will be the following. First it will be assumed that the animal is a horse. Then, using rules, it will be possible to state that “The animal has no hooves and no dewlaps”, which allows to break this rule into two next premises: “The animal has no hooves” and “The animal has no dewlaps”. Both premises hold, since the facts prove this; therefore, the selected animal is a horse.
Forward chaining is a different method of reasoning; in this case, reasoning starts with the data. The data are combined to receive additional data and the rules are applied to derive more data (Kendal & Creen, 2007). This process is repeated until the goal is achieved (Kendal & Creen, 2007). In the above-mentioned example with the identification of an animal, the process will be the following. Combining first and second fact, one can get that the target animal has neither dewlaps no horns. The first rule does not apply to the animal then, since the “if” part of the statement does not hold. The second rule applies to the animal, so it is possible to state that this is a horse.
Question 2. Collaborative Space – Colossus is an expert system developed in Australia to guide insurance claim assessors. http://www2.austlii.edu.au/~graham/publications/1992/ALJColossus.html. What have been the advantages of the system? What issues or problems have arisen?
The purpose of Colossus is to assess bodily injury claims and to assist insurers in handling claims. The development of an expert system for this purpose was driven by extremely high variability of claim assessment by different assessors and the overall complexity of considering claims. The system has a number of notable advantages: it carefully assesses all aspects of the injury and takes into account related circumstances, it develops the assessment basing on five categories of factors (trauma, disability, impairment, loss of life enjoyment, disfigurement) (Greenleaf¸ 1992). Colossus produces a convenient two-page report where the data provided by the assessor and the reasoning used by the system is provided in a structured way. This system allows to reduce costs and variability of assessing insurance claims and helps to make decisions using the available legislation base.
At the same time, the system does have several problems and issues. First of all, it poses up to 700 questions to the respondent (Greenleaf¸ 1992), and the decision made by Colossus strongly depends on the quality of answers provided by the assessor. The system leaves the evaluation of disfigurement to the assessor. The report does not provide details of injury assessment according to the categories of factors, and provides little background of the reasoning. Furthermore, Colossus cannot take into account special circumstances which are not entered into the expert system and are not included into rules.
Topic 10. Intelligent Agents
Question 1. Find and describe an example of an intelligent agent software and describe its level of intelligence, mobility and agency (autonomy).
Intelligent agent software relates to such software which helps users to automate some tasks with a certain degree of autonomy and realize users’ goals in a complex and multifaceted environment. Intelligent agents differ by their level of intelligence, level of autonomy and mobility (Kendal & Creen, 2007). With regard to intelligence level, it is possible to classify agents into straight-order agents, agents based on user-initiated search, software agents based on user profiles and agents with reasoning capabilities. Autonomy refers to the degree of control that the agent has over its actions. It is possible to identify goal-oriented, collaborative, flexible and self-starting types of agents according to the degree of autonomy (Kendal & Creen, 2007). The degree of mobility describes the extent to which the software can travel over the network; it is possible to classify agents as static (fixed on specific computers), agents with limited mobility (i.e. agents operating within a specific intranet) and mobile agents (agents travel over the whole network and can be transferred to any computer).
One intelligent agent software is Travelocity (www.travelosity.com). This agent is intended for planning travels and retrieving travel-related information. The agent is implemented in the form of a website and has access to a huge database with various kinds of travel information, starting with tickets and tours, ending with photos, maps, videos, bookings, reservations, etc. In terms of intelligence, this agent is based on user profile and user preferences, so it can be classified as level 2 agent. The choices selected by the agent are based on user’s settings and are selected in accordance with user requirements and similar searches performed by other users. The degree of control for this agent is collaborative: user settings and existing knowledge are both used to develop new solutions. As for mobility, Travelocity can be viewed as a fully mobile agent.
Question 2. Collaborative Space – What problems or issues can arise with the use of intelligent agent software? (Also complete this in the collaborative space online)
The use of intelligent agent software is very promising with regard to data processing and coping with information overload. However, there might emerge certain issues associated with the use of intelligent agents. First of all, the speed of technological development and the extent to which information technologies are changing might make the creation of new agents questionable. For example, if the information becomes outdated when it is processed by the agent, or the agent itself becomes obsolete when it is launched, it might be not reasonable to create intelligent agent software at all. In such conditions, only the agents that are economically efficient and socially desirable will remain.
Potential threats of the development of intelligent agent software include privacy and security issues and ethical issues (Turban, Aronson & Ting-Peng, 2005). Agents might collect or accumulate user-specific data, and it is difficult to ensure that agents are not making some unwanted operations with the data. Users might lose control over sensitive information due to using intelligent agents. Furthermore, intelligent agent software might intentionally or unintentionally violate user privacy. There are no specific regulations guiding the use of intelligent agents, so there might emerge ethical issues associated with the actions or conclusions of intelligent agents (for example, if one of family members uses an agent to choose the best clinic to cure a disease and the same agent recommends preventive cures from the disease to other family members, therefore implicitly disclosing health problems of one family member to others).
One more potential source of issues is the reliability of decisions or recommendations provided by the agent, and the liability associated with these decisions. On one hand, people might have too high expectations with regard to such agents and rely on software judgments too much. On the other hand, agents might provide false, inaccurate or discriminatory information, and it is hardly possible to verify to which extent the agent’s information is reliable.
Question 3. Wolfram Alpha is a computational knowledge engine. How could a business like a Bank use some of its features? What might be some issues?
Wolfram Alpha is a knowledge engine that actually compiles the answer instead of providing search results like typical search engines do. This system is frequently used for mathematical computations, visualization and other queries involving computations. Wolfram Alpha is very efficient in performing complex queries and retrieving its results, in performing localizations and currency conversions, in calculating different values with good precision and comparing the results of calculations. This system in its original form was intended for individual users but currently Wolfram Alpha also has different business options and solutions.
Wolfram Alpha could be used by banks in various ways. First of all, this system can be used for developing computational widgets or small applications enhancing existing functionality. Secondly, Wolfram Alpha can be used for performing financial computations using actual data and specific precision. Furthermore, Wolfram Alpha offers such services as deploying the system in a custom environment, using Wolfram Alpha technology to record data and to curate it therefore enabling computing possibilities for enterprises, creating Wolfram Alpha interface for corporate systems and downloading a private copy of Wolfram Alpha data for internal use.
All of the above-mentioned features could be used by a Bank. However, there might emerge certain issues. Key issues include questionable reliability of the computations and potential security problems. As for reliability, it would be necessary to verify the accuracy of computations, and it is not clear how to do this in an efficient way. As for security, all data will be searchable which means that data exposure to internal and external threats will increase. There might also emerge issues of establishing different levels of data access and different computational output which might emerge due to unavailability of certain data.
Question 4. What are the advantages of using Protégé OWL compared to Protégé? What are the main differences?
Protégé is a system for knowledge acquisition and for working with ontologies. This is an open-source project developed at Stanford University which provides a convenient visual interface for creating ontologies, validating the consistency of models and making new inferences using deductive reasoning. While Protégé represents a large platform for developing ontologies, Protégé OWL is a tool specifically developed for the semantic web.
Protégé OWL is an extension for Protégé system (distributed as a plug-in) which helps users to create and edit their ontologies using the Web Ontology Language (OWL). OWL is gradually becoming a standard for representing knowledge, and Protégé therefore helps its users make their ontologies more universally accessible and available for sharing. Furthermore, Protégé OWL allows using the functionality of Protégé system such as diverse storage formats, tools for acquiring and visualizing data, convenient GUI, user forms and tools for data acquisition, etc (Rubin, Knublauch & Musen, 2005). Protégé OWL has a separate API and can therefore be integrated into other applications.
Key advantages of using Protégé OWL include the inclusion of both knowledge representation and encoding of reasoning rules in standardized form, the use of reasoning enhanced due to automation, the availability of wizards for handling complex tasks, the presence of a graphical editor for managing logical expressions in OWL, etc (Rubin, Knublauch & Musen, 2005). Therefore, Protégé OWL extends Protégé and helps automate the creation of ontologies.
Topic 11. Document and Content Management Systems
Question 1. According to Sprague (see article on BlackBoard), what is the business value of documents?
Sprague uses the definition of document outlined by Levlen – a document is a unit of recorded information structured for human consumption (Sprague, 1995). Furthermore, the author names two approaches to perceiving business value of documents. The first category of document value can be found in the companies which use documents as products or generate revenue due to documents. For example, companies selling directories and reference books directly benefit from documents and therefore effective document management might create additional value for such companies.
The second source of documents’ business value is the improvement of information management and the consequent growth of operating and management efficiency. Sprague (1995) refers to this category of business value as “the value to support organizational performance”. Furthermore, Sprague (1995) identifies three general types of situations when document management can be used to enhance organizational performance. These are the improvement of managing and communicating ideas and concepts within the organization, reengineering of key business processes and leveraging organizational memory (Sprague, 1995). In the first case, improved organizational mechanisms for storing and sharing ideas create numerous opportunities for organizational learning and growth. In the second case, optimization of document management might lead to reconsideration of business processes and to notable organizational improvement. In the third situation, organizational learning can be enhanced due to better and more in-depth analysis of organizational activities.
Question 2. Documents can be thought of as having a lifecycle from creation through to disposal. How can a document management system support this lifecycle? Give an example with respect to a cloud based commercial technology.
Documents have their own life cycle which consists of the following stages: creating, capturing, indexing, managing, accessing, retrieving, administering, reusing/repurposing, sharing and collaborating, distributing, retaining, disposing and preserving (Smallwood, 2013). These steps can be identified both in traditional paper document management systems and in electronic document management systems. It is possible to consider the example of a cloud-based document management solution such as eFileCabinet.
eFileCabinet document management system is a commercial cloud solution for managing documents. It allows to create and upload documents into the system, therefore supporting the first two steps of document life cycle. Furthermore, it allows to create metadata to the documents or add tags along with other metadata manually (Smallwood, 2013). This is a reflection of the indexing stage of document life cycle. It is easy to manage and administer documents using eFileCabinet because this system offers different user roles and different rules for accessing the document. The owner of the document can set access or viewing rights and share the document with other users (reusing/repurposing, sharing and collaborating stages of document life cycle). There also exist various opportunities for sending the documents, compressing them, archiving, caching, etc.
The functions for accessing and retrieving documents in cloud document management systems such as eFileCabinet are significantly wider compared to traditional paper document management. Users can access documents from virtually any location, can search their documents, retrieve and update them, perform data mining, print documents, add electronic signatures and certificates, encrypt documents and distribute them. It is also easy to create new versions of documents, to initiate collaboration and sharing in real time (Smallwood, 2013), to distribute documents in various forms, to make copies of documents and back up documents for long-term storage purposes. The function of disposing is also implemented in eFileCabinet as it is possible to delete documents in a secure way.
Question 3. The MindServer technology (see BlackBoard) provides some interesting features and advantages over ontology based approaches. What are these features and advantages?
MindServer technology is an innovative system of document management which is developed by Recommind, a leading manufacturer of information risk management software with powerful search capabilities. MindServer can analyze electronic information and identify documents which should be preserved for legal purposes (especially in the case of litigation or other legal proceedings) (PR Newswire, 2013). MindServer can detect enterprise-related information that might be potentially relevant to the considered situations; it can lock down documents and send its search results to other applications. MindServer can even transfer its query results into databases or content management systems. This platform has a number of advanced features such as convenient user interface for document filtering, e-discovery options, scalability, opportunities for searching even in complex documentation or projects, etc. (PR Newswire, 2013)
MindServer is able to learn by studying user data and behaviors and has predictive capabilities (PR Newswire, 2013). Due to these features, users can access and manage the relevant documents quickly and effortlessly. MindServer works with a variety of information repositories and notably reduces the cost of conducting legal research and contributes to the improvement of quality of such research. MindServer also creates a collaborative environment where users can use combined organizational expertise to develop best solutions and to deal with documents in an intelligent way.
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"The terms offer and acceptance." freeessays.club, 17 May 2016
"The terms offer and acceptance." freeessays.club, 17 May 2016