What is the name of the system that helps organizations manage structured semistructured and unstructured types of information?

When a conversation turns to analytics or big data, the terms structured, semi-structured and unstructured might get bandied about. These are classifications of data that are now important to understand with the rapid increase of semi-structured and unstructured data today as well as the development of tools that make managing and analyzing these classes of data possible. Here’s what you need to know.

What’s The Difference Between Structured, Semi-Structured And Unstructured Data?

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Structured Data

Data that is the easiest to search and organize, because it is usually contained in rows and columns and its elements can be mapped into fixed pre-defined fields, is known as structured data. Think about what data you might store in an Excel spreadsheet and you have an example of structured data. Structured data can follow a data model a database designer creates - think of sales records by region, by product or by customer. In structured data, entities can be grouped together to form relations (‘customers’ that are also ‘satisfied with the service). This makes structured data easy to store, analyze and search and until recently was the only data easily usable for businesses. Today, most estimate structured data accounts for less than 20 percent of all data.

Often structured data is managed using Structured Query Language (SQL)—a programming software language developed by IBM in the 1970s for relational databases.

Structured data can be created by machines and humans. Examples of structured data include financial data such as accounting transactions, address details, demographic information, star ratings by customers, machines logs, location data from smart phones and smart devices, etc.

Unstructured Data

A much bigger percentage of all the data is our world is unstructured data. Unstructured data is data that cannot be contained in a row-column database and doesn’t have an associated data model. Think of the text of an email message. The lack of structure made unstructured data more difficult to search, manage and analyse, which is why companies have widely discarded unstructured data, until the recent proliferation of artificial intelligence and machine learning algorithms made it easier to process.

Other examples of unstructured data include photos, video and audio files, text files, social media content, satellite imagery, presentations, PDFs, open-ended survey responses, websites and call center transcripts/recordings.

Instead of spreadsheets or relational databases, unstructured data is usually stored in data lakes, NoSQL databases, applications and data warehouses. The wealth of information in unstructured data is now accessible and can be automatically processed with artificial intelligence algorithms today. This technology has elevated unstructured data to an extremely valuable resource for organizations.

Semi-Structured Data

Beyond structured and unstructured data, there is a third category, which basically is a mix between both of them. The type of data defined as semi-structured data has some defining or consistent characteristics but doesn’t conform to a structure as rigid as is expected with a relational database. Therefore, there are some organizational properties such as semantic tags or metadata to make it easier to organize, but there’s still fluidity in the data.

Email messages are a good example. While the actual content is unstructured, it does contain structured data such as name and email address of sender and recipient, time sent, etc. Another example is a digital photograph. The image itself is unstructured, but if the photo was taken on a smart phone, for example, it would be date and time stamped, geo tagged, and would have a device ID. Once stored, the photo could also be given tags that would provide a structure, such as ‘dog’ or ‘pet.’

A lot of what people would usually classify as unstructured data is indeed semi-structured, because it contains some classifying characteristics.

The Difference Between Structured, Unstructured, And Semi-Structured Data

To easily understand the differences between the classifications of data, let’s use this analogy to illustrate. When interviewing for a job, let’s say there are three different classifications of interviews: structured, semi-structured and unstructured.

In a structured interview, the interviewer follows a strict script that was defined by the human resources department and is followed for every candidate. Another form of interview is an unstructured interview. In an unstructured interview, it is entirely up to the interviewer to determine the questions and the order they will be asked (or even if they will be asked) for every candidate. A semi-structured interview takes elements from both structured and unstructured interview classifications. It uses the consistency and quantitative elements allowed with the structured interview but offers the freedom to customize based on the circumstances that are more in line with an unstructured interview.

So, for data, structured data is easily organizable and follows a rigid format; unstructured is complex and often qualitative information that is impossible to reduce to or organize in a relational database and semi-structured data has elements of both.

Information system that supports business or organizational decision-making activities

Example of a decision support system for John Day Reservoir.

Recommender systems Concepts Methods and challenges Implementations Research

Collective intelligence Relevance Star ratings Long tail

Cold start Collaborative filtering Dimensionality reduction Implicit data collection Item-item collaborative filtering Matrix factorization Preference elicitation Similarity search

Collaborative search engine Content discovery platform Decision support system Music Genome Project Product finder

GroupLens Research MovieLens Netflix Prize

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A decision support system (DSS) is an information system that supports business or organizational decision-making activities. DSSs serve the management, operations and planning levels of an organization (usually mid and higher management) and help people make decisions about problems that may be rapidly changing and not easily specified in advance—i.e. unstructured and semi-structured decision problems. Decision support systems can be either fully computerized or human-powered, or a combination of both.

While academics have perceived DSS as a tool to support decision making processes, DSS users see DSS as a tool to facilitate organizational processes.[1] Some authors have extended the definition of DSS to include any system that might support decision making and some DSS include a decision-making software component; Sprague (1980)[2] defines a properly termed DSS as follows:

1. DSS tends to be aimed at the less well structured, underspecified problem that upper level managers typically face;
2. DSS attempts to combine the use of models or analytic techniques with traditional data access and retrieval functions;
3. DSS specifically focuses on features which make them easy to use by non-computer-proficient people in an interactive mode; and
4. DSS emphasizes flexibility and adaptability to accommodate changes in the environment and the decision making approach of the user.

DSSs include knowledge-based systems. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, and personal knowledge, or business models to identify and solve problems and make decisions.

Typical information that a decision support application might gather and present includes:

• inventories of information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
• comparative sales figures between one period and the next,
• projected revenue figures based on product sales assumptions.

History

The concept of decision support has evolved mainly from the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the implementation work done in the 1960s.[3] DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s.

In the middle and late 1980s, executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS. According to Sol (1987),[4] the definition and scope of DSS have been migrating over the years: in the 1970s DSS was described as "a computer-based system to aid decision making"; in the late 1970s the DSS movement started focusing on "interactive computer-based systems which help decision-makers utilize data bases and models to solve ill-structured problems"; in the 1980s DSS should provide systems "using suitable and available technology to improve effectiveness of managerial and professional activities", and towards the end of 1980s DSS faced a new challenge towards the design of intelligent workstations.[4]

In 1987, Texas Instruments completed development of the Gate Assignment Display System (GADS) for United Airlines. This decision support system is credited with significantly reducing travel delays by aiding the management of ground operations at various airports, beginning with O'Hare International Airport in Chicago and Stapleton Airport in Denver Colorado.[5] Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS. As the turn of the millennium approached, new Web-based analytical applications were introduced.

DSS also have a weak connection to the user interface paradigm of hypertext. Both the University of Vermont PROMIS system (for medical decision making) and the Carnegie Mellon ZOG/KMS system (for military and business decision making) were decision support systems which also were major breakthroughs in user interface research. Furthermore, although hypertext researchers have generally been concerned with information overload, certain researchers, notably Douglas Engelbart, have been focused on decision makers in particular.

The advent of more and better reporting technologies has seen DSS start to emerge as a critical component of management design. Examples of this can be seen in the intense amount of discussion of DSS in the education environment.

Applications

DSS can theoretically be built in any knowledge domain. One example is the clinical decision support system for medical diagnosis. There are four stages in the evolution of clinical decision support system (CDSS): the primitive version is standalone and does not support integration; the second generation supports integration with other medical systems; the third is standard-based, and the fourth is service model-based.[6]

DSS is extensively used in business and management. Executive dashboard and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources. Due to DSS, all the information from any organization is represented in the form of charts, graphs i.e. in a summarized way, which helps the management to take strategic decisions. For example, one of the DSS applications is the management and development of complex anti-terrorism systems.[7] Other examples include a bank loan officer verifying the credit of a loan applicant or an engineering firm that has bids on several projects and wants to know if they can be competitive with their costs.

A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. Agricultural DSSes began to be developed and promoted in the 1990s.[8] For example, the DSSAT4 package,[9] The Decision Support System for Agrotechnology Transfer[10] developed through financial support of USAID during the 80s[citation needed] and 90s, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels. Precision agriculture seeks to tailor decisions to particular portions of farm fields. There are, however, many constraints to the successful adoption of DSS in agriculture.[11]

DSS is also prevalent in forest management where the long planning horizon and the spatial dimension of planning problems demand specific requirements. All aspects of Forest management, from log transportation, harvest scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. In this context, the consideration of single or multiple management objectives related to the provision of goods and services that are traded or non-traded and often subject to resource constraints and decision problems. The Community of Practice of Forest Management Decision Support Systems provides a large repository on knowledge about the construction and use of forest Decision Support Systems.[12]

A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, the Canadian National Railway system managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.

DSS has been used for risk assessment to interpret monitoring data from large engineering structures such as dams, towers, cathedrals, or masonry buildings. For instance, Mistral is an expert system to monitor dam safety, developed in the 1990s by Ismes (Italy). It gets data from an automatic monitoring system and performs a diagnosis of the state of the dam. Its first copy, installed in 1992 on the Ridracoli Dam (Italy), is still operational 24/7/365.[13] It has been installed on several dams in Italy and abroad (e.g., Itaipu Dam in Brazil),[14] and on monuments under the name of Kaleidos.[15] Mistral is a registered trade mark of CESI. GIS has been successfully used since the ‘90s in conjunction with DSS, to show on a map real-time risk evaluations based on monitoring data gathered in the area of the Val Pola disaster (Italy). [16]

Components

Design of a drought mitigation decision support system

Three fundamental components of a DSS architecture are:[17][18][19][20][21]

1. the database (or knowledge base),
2. the model (i.e., the decision context and user criteria)
3. the user interface.

The users themselves are also important components of the architecture.[17][21]

Taxonomies

Using the relationship with the user as the criterion, Haettenschwiler[17] differentiates passive, active, and cooperative DSS. A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions. An active DSS can bring out such decision suggestions or solutions. A cooperative DSS allows for an iterative process between human and system towards the achievement of a consolidated solution: the decision maker (or its advisor) can modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation, and likewise the system again improves, completes, and refines the suggestions of the decision maker and sends them back to them for validation.

Another taxonomy for DSS, according to the mode of assistance, has been created by D. Power:[22] he differentiates communication-driven DSS, data-driven DSS, document-driven DSS, knowledge-driven DSS, and model-driven DSS.[18]

• A communication-driven DSS enables cooperation, supporting more than one person working on a shared task; examples include integrated tools like Google Docs or Microsoft SharePoint Workspace.[23]
• A data-driven DSS (or data-oriented DSS) emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.
• A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats.
• A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures or in similar structures like interactive decision trees and flowcharts.[18]
• A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data-intensive. Dicodess is an example of an open-source model-driven DSS generator.[24]

Using scope as the criterion, Power[25] differentiates enterprise-wide DSS and desktop DSS. An enterprise-wide DSS is linked to large data warehouses and serves many managers in the company. A desktop, single-user DSS is a small system that runs on an individual manager's PC.

Development frameworks

Similarly to other systems, DSS systems require a structured approach. Such a framework includes people, technology, and the development approach.[19]

The Early Framework of Decision Support System consists of four phases:

• Intelligence – Searching for conditions that call for decision;
• Design – Developing and analyzing possible alternative actions of solution;
• Choice – Selecting a course of action among those;
• Implementation – Adopting the selected course of action in decision situation.

DSS technology levels (of hardware and software) may include:

1. The actual application that will be used by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem.
2. Generator contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems such as Crystal, Analytica and iThink.
3. Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules

An iterative developmental approach allows for the DSS to be changed and redesigned at various intervals. Once the system is designed, it will need to be tested and revised where necessary for the desired outcome.

Classification

There are several ways to classify DSS applications. Not every DSS fits neatly into one of the categories, but may be a mix of two or more architectures.

Holsapple and Whinston[26] classify DSS into the following six frameworks: text-oriented DSS, database-oriented DSS, spreadsheet-oriented DSS, solver-oriented DSS, rule-oriented DSS, and compound DSS. A compound DSS is the most popular classification for a DSS; it is a hybrid system that includes two or more of the five basic structures.[26]

The support given by DSS can be separated into three distinct, interrelated categories:[27] Personal Support, Group Support, and Organizational Support.

DSS components may be classified as:

1. Inputs: Factors, numbers, and characteristics to analyze
2. User knowledge and expertise: Inputs requiring manual analysis by the user
3. Outputs: Transformed data from which DSS "decisions" are generated
4. Decisions: Results generated by the DSS based on user criteria

DSSs which perform selected cognitive decision-making functions and are based on artificial intelligence or intelligent agents technologies are called intelligent decision support systems (IDSS)[28]

The nascent field of decision engineering treats the decision itself as an engineered object, and applies engineering principles such as design and quality assurance to an explicit representation of the elements that make up a decision.

See also

Wikimedia Commons has media related to Decision support systems.

• Argument map
• Cognitive assets (organizational)
• Decision theory
• Enterprise decision management
• Expert system
• Judge–advisor system
• Knapsack problem
• Land allocation decision support system
• List of concept- and mind-mapping software
• Morphological analysis (problem-solving)
• Online deliberation
• Participation (decision making)
• Predictive analytics
• Project management software
• Self-service software
• Spatial decision support system
• Strategic planning software

References

1. ^ Keen, Peter (1980). "Decision support systems : a research perspective". Cambridge, Massachusetts : Center for Information Systems Research, Alfred P. Sloan School of Management. hdl:1721.1/47172. {{cite journal}}: Cite journal requires |journal= (help)
2. ^ Sprague, R;(1980). "A Framework for the Development of Decision Support Systems." MIS Quarterly. Vol. 4, No. 4, pp.1-25.
3. ^ Keen, P. G. W. (1978). Decision support systems: an organizational perspective. Reading, Mass., Addison-Wesley Pub. Co. ISBN 0-201-03667-3
4. ^ a b Henk G. Sol et al. (1987). Expert systems and artificial intelligence in decision support systems: proceedings of the Second Mini Euroconference, Lunteren, The Netherlands, 17–20 November 1985. Springer, 1987. ISBN 90-277-2437-7. p.1-2.
5. ^ Efraim Turban; Jay E. Aronson; Ting-Peng Liang (2008). Decision Support Systems and Intelligent Systems. p. 574.
6. ^ Wright, A; Sittig, D (2008). "A framework and model for evaluating clinical decision support architectures q". Journal of Biomedical Informatics. 41 (6): 982–990. doi:10.1016/j.jbi.2008.03.009. PMC 2638589. PMID 18462999.
7. ^ Zhang, S.X.; Babovic, V. (2011). "An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions". Decision Support Systems. 51 (1): 119–129. doi:10.1016/j.dss.2010.12.001. S2CID 15362734.
8. ^ Papadopoulos, A.P.; Shipp, J.L; Jarvis, William R.; Jewett, Thomas J.; Clarke, N.D. (1 July 1995). "The Harrow Expert System for Greenhouse Vegetables". HortScience. American Society for Horticultural Science. 30 (4): 846F–847. doi:10.21273/HORTSCI.30.4.846F. ISSN 0018-5345.
9. ^ "DSSAT4 (pdf)" (PDF). Archived from the original (PDF) on 27 September 2007. Retrieved 29 December 2006.
10. ^ "Official Home of the DSSAT Cropping Systems Model". DSSAT.net. Retrieved 19 August 2021.
11. ^ Stephens, W. and Middleton, T. (2002). Why has the uptake of Decision Support Systems been so poor? In: Crop-soil simulation models in developing countries. 129-148 (Eds R.B. Matthews and William Stephens). Wallingford:CABI.
12. ^ Community of Practice Forest Management Decision Support Systems, //www.forestdss.org/
13. ^ Salvaneschi, Paolo; Cadei, Mauro; Lazzari, Marco (1996). "Applying AI to structural safety monitoring and evaluation". IEEE Expert. 11 (4): 24–34. doi:10.1109/64.511774. Retrieved 5 March 2014.
14. ^ Masera, Alberto; et al. "Integrated approach to dam safety". Comitê Brasileiro de Barragens. Retrieved 16 December 2020.
15. ^ Lancini, Stefano; Lazzari, Marco; Masera, Alberto; Salvaneschi, Paolo (1997). "Diagnosing Ancient Monuments with Expert Software" (PDF). Structural Engineering International. 7 (4): 288–291. doi:10.2749/101686697780494392.
16. ^ Lazzari, M.; Salvaneschi, P. (1999). "Embedding a Geographic Information System in a Decision Support System for Landslide Hazard Monitoring" (PDF). Natural Hazards. 20 (2–3): 185–195. doi:10.1023/A:1008187024768. S2CID 1746570.
17. ^ a b c Haettenschwiler, P. (1999). Neues anwenderfreundliches Konzept der Entscheidungsunterstützung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich, vdf Hochschulverlag AG: 189-208.
18. ^ a b c Power, D. J. (2002). Decision support systems: concepts and resources for managers. Westport, Conn., Quorum Books.
19. ^ a b Sprague, R. H. and E. D. Carlson (1982). Building effective decision support systems. Englewood Cㄴliffs, N.J., Prentice-Hall. ISBN 0-13-086215-0
20. ^ Haag, Cummings, ㅊㄴㅋMcCubbrey, Pinsonneault, Donovan (2000). Management Informatㅍㅈion Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0-07-281947-2
21. ^ a b Marakas, G. M. (1999). Decision support systems in the twenty-first century. Upper Saddle River, N.J., Prentice Hall.
22. ^ "Decision Support Systems (DSS) Articles On-Line".
23. ^ Stanhope, Phil (2002). Get in the Groove: Building Tools and Peer-to-Peer Solutions with the Groove Platform. ACM Digital Library. ISBN 9780764548932. Retrieved 30 October 2019.
24. ^ Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF.
25. ^ Power, D. J. (1996). What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3).
26. ^ a b Holsapple, C.W., and A. B. Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing. ISBN 0-324-03578-0
27. ^ Hackathorn, R. D., and P. G. W. Keen. (1981, September). "Organizational Strategies for Personal Computing in Decision Support Systems." MIS Quarterly, Vol. 5, No. 3.
28. ^ F. Burstein; C. W. Holsapple (2008). Handbook on Decision Support Systems. Berlin: Springer Verlag.

Further reading

• Marius Cioca, Florin Filip (2015). Decision Support Systems - A Bibliography 1947-2007.
• Borges, J.G, Nordström, E.-M. Garcia Gonzalo, J. Hujala, T. Trasobares, A. (eds). (2014). " Computer-based tools for supporting forest management. The experience and the expertise world-wide. Dept of Forest Resource Management, Swedish University of Agricultural Sciences. Umeå. Sweden.
• Delic, K.A., Douillet, L. and Dayal, U. (2001) "Towards an architecture for real-time decision support systems:challenges and solutions.
• Diasio, S., Agell, N. (2009) "The evolution of expertise in decision support technologies: A challenge for organizations," cscwd, pp. 692–697, 13th International Conference on Computer Supported Cooperative Work in Design, 2009. //web.archive.org/web/20121009235747///www.computer.org/portal/web/csdl/doi/10.1109/CSCWD.2009.4968139
• Gadomski, A.M. et al.(2001) "An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers Archived 5 March 2016 at the Wayback Machine", Int. J. Risk Assessment and Management, Vol. 2, Nos. 3/4.
• Gomes da Silva, Carlos; Clímaco, João; Figueira, José (2006). "A scatter search method for bi-criteria {0,1}-knapsack problems". European Journal of Operational Research. Elsevier BV. 169 (2): 373–391. doi:10.1016/j.ejor.2004.08.005. ISSN 0377-2217.
• Ender, Gabriela; E-Book (2005–2011) about the OpenSpace-Online Real-Time Methodology: Knowledge-sharing, problem solving, results-oriented group dialogs about topics that matter with extensive conference documentation in real-time. Download //web.archive.org/web/20070103022920///www.openspace-online.com/OpenSpace-Online_eBook_en.pdf
• Jiménez, Antonio; Ríos-Insua, Sixto; Mateos, Alfonso (2006). "A generic multi-attribute analysis system". Computers & Operations Research. Elsevier BV. 33 (4): 1081–1101. doi:10.1016/j.cor.2004.09.003. ISSN 0305-0548.
• Jintrawet, Attachai (1995). "A Decision Support System for Rapid Assessment of Lowland Rice-based Cropping Alternatives in Thailand". Agricultural Systems. 47 (2): 245–258. doi:10.1016/0308-521X(94)P4414-W.
• Matsatsinis, N.F. and Y. Siskos (2002), Intelligent support systems for marketing decisions, Kluwer Academic Publishers.
• Omid A.Sianaki, O Hussain, T Dillon, AR Tabesh - ... Intelligence, Modelling and Simulation (CIMSiM), 2010, Intelligent decision support system for including consumers' preferences in residential energy consumption in smart grid
• Power, D. J. (2000). Web-based and model-driven decision support systems: concepts and issues. in proceedings of the Americas Conference on Information Systems, Long Beach, California.
• Reich, Yoram; Kapeliuk, Adi (2005). "A framework for organizing the space of decision problems with application to solving subjective, context-dependent problems". Decision Support Systems. Elsevier BV. 41 (1): 1–19. doi:10.1016/j.dss.2004.05.001. ISSN 0167-9236.
• Sauter, V. L. (1997). Decision support systems: an applied managerial approach. New York, John Wiley. ISBN 978-0471173359
• Silver, M. (1991). Systems that support decision makers: description and analysis. Chichester ; New York, Wiley.
• Sprague, Ralph (1986). Decision support systems : putting theory into practice. Englewood Cliffs, N.J: Prentice-Hall. ISBN 978-0-13-197286-5. OCLC 13123699.

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