Database Technologies

Strategic alignment of IT resources – A case study in Grocery industry (Part 2)

Strategic alignment of IT resources – A case study in Grocery industry (Part 2)

 

	Japan	UK	Spain	US
Effect of economic situation	The effect of recession has prompted Japanese to be more cautious while selecting grocery stores to shop. This situation is breeding ground for underdeveloped private label market in Japan to capitalize on price (32).	Consumers have scaled back spending and repaid debt amid signs of slow recovery. In Q4 of 2009, the UK economy experienced the first economic expansion in six quarters. Low consumer confidence, high personal and government debt, and high taxes will slow recovery (48).	Recession has its impacts on buying habits on Spanish people too. While looking out for cheaper private labels, they also have increased quality sense (60).	Currently, there is high trends that shows shift from eating out to going back to grocery stores and save money (usage coupons etc). There exists high food prices, down economy, low consumer confidence and notion of ‘healthy eating which is required and costly (4).
Technology & Infrastructure Issues	Concerns that old style of doing business is not viable any more. Trying to improve innovation systems (33).	Robust private markets for technology and services. Strong government support for capturing value in public sector (49).	Innovation system highly dependent on foreign technology. Deficit appears to be structural (61).	Major technology thrust pushes industry to take drastic steps towards customer satisfaction and innovation (5).
Integration Issues	System integration issues due to heavy mergers between companies and due to suppliers’ legacy systems.	Applications developers must understand retailers’ business and integrate with its existing systems as part of its value proposition.	Severe integration issues in supply chain.	Major challenge is integration issues while targeting global sourcing and thus being exposed to wider pool of suppliers, providing multi-channel integration for customers and integration with legacy systems.

	Japan	UK	Spain	US
Political Structure	A parliamentary government with a constitutional monarchy. Legal system is modeled after European civil law systems with English-American influence; judicial review of legislative acts in the Supreme Court. Supreme Court (chief justice is appointed by the monarch after designation by the cabinet; all other justices are appointed by the cabinet) (34).	Constitutional monarchy and Commonwealth realm (50).	Basque Nationalist Party , Canarian Convergence and Union Coalition, Democratic Union of Catalonia (63).	Even though current US political setup and administration is convinced that supporting innovation and entrepreneurship is necessary for improving the economic conjuncture, the existing economic recession cause consumer product manufacturers to reconsider their plans to launch new products for Grocery industry (6,7).
Legislation	In 2006, the Internet providers attempted to disconnect users anytime they detected P2P or any other file-sharing software. The Japanese Ministry attempted to block P2P but stopped because of concerns of privacy issues (35).	Growing dominance of large grocery chains prompted Office of Fair Trading to review competitive practices of largest retailers (62). Fear that smaller suppliers and sole proprietorships will be pushed out of markets has prompted lawmakers to consider limiting labor and operational tactics used by large grocers.	Education expenditures amount to 4.2% of the population, which compares to 97th in the world. By law, every public school in the country is required to teach Roman Catholicism. But new legislation has been enacted which has made religious classes optional (64).	Current US administration has put forth various legislative measures to support product innovation and growth of grocery industry, especially when Grocery Manufacturers association is influencing US Senate to modernize U.S. Chemical Safety Laws (8).
Trade Policy	Japan’s weighted average tariff rate was 1.3 percent in 2008. Import and export bans and restrictions, import quotas and licensing, services market access barriers, non-transparent and burdensome regulations and standards, restrictive sanitary and phytosanitary rules, restrictions in government procurement, state trade in some goods, subsidies, and inefficient customs administration add to the cost of trade (36).	The Bank of England periodically coordinates interest rate moves with the European Central Bank, but Britain remains outside the European Economic and Monetary Union (50).	Spain’s trade policy is the same as that of other members of the European Union. The common EU weighted average tariff rate was 1.3 percent in 2008. However, the EU has high or escalating tariffs for agricultural and manufacturing products, and its MFN tariff code is complex.	US trade policies are designed to support grocery industry expansion in developing areas and to reduce overhead for Grocery stores and put in more money for product innovation (4).

 

Porter’s Competitive Forces Model

 

Dominant Blueprints & Strategic Focus

 

Blueprint  (Drivers & Constraints)

Blueprint	Force	Japan	UK	Spain	US
Multi-Channel Easy To Do Business With	Driver	Acquisition of small wholesalers, mergers and cooperative agreements are in response to need for greater efficiency (39).	Growing interest in shopping online makes retailers pursue web strategies that compliment brick & mortar experience(47).	Efficiency of marketing to consumers who often make decision to buy at point-of-sale(65).	Technology/Innovation infusion in the form of Web 2.0 and mobile technology offers wide options for channel integration and is leading blueprint’s development and alignment of IT (20).
	Constraint	Shortage of technical capability, attitudinal problems preventing wholesalers, retailers, and manufacturers from working together. Poor penetration of technology in traditional-bound portions of value chain (39).	Ordering more efficient with RFID(51). Loyalty programs track individual consumers‘ and tailor promotions (52).	Today, more grocers are collecting customer-specific data. Large retailers using RFID to deliver targeted promotions (66).	Convincing customers to thinking “My Store” rather than “The store“. Greater reliance on integration for increased sales, customer retention and profitability (16,17,18,19).
Spend Management Low Cost	Driver	The market has shifted its focus to support more discount store models in order to provide lower prices to customer (38).	Spend management encourages higher quality and greater choice (Profit through partnership, 1994), faster replenishment.	Spend management software has been delivered to the enterprise however, Europe was a unique case because of the large number of languages owed to its significant presence in both Western and Eastern Europe (67).	Economic recession compels companies to cut costs on direct and indirect goods/services and they resort to spend management solutions to gain visibility into the area of procurement and analysis(US21).
	Constraint	Recession is having a major negative impact on consumer confidence, as they look for ways to cut back on daily expenditure and retailers have to drastically rethink their operating strategies in an effort to retain even moderate growth levels (38).	Cash purchases without loyalty card provide no customer data to improve decisions.	Mega-Hubs were launched with promise to provide interoperability between trading exchanges, however companies were not ready to invest into the process because it was a separate entity (67).	Walmart’s efforts to align IT to current spend management blueprint is partly hindered by cultural/language barriers while on its journey to global sourcing (23).
Employee   Productivity Multiplier	Driver	A decade of declining economic growth aligned with contextual factors, such as an ageing workforce, has caused Japanese firms to introduce changes to their HRM strategies (40).	Respecting employee rights to privacy and confidentiality requires controls that limit data exchange between applications and by information consumer.	Integrating and automating HR processes and multiple country systems is demanding for service providers (68).	Increased need to reduce the total cost of employee communication per year , improve corporate hiring process and increased productivity improvement ,all influence usage of employee centric blueprint (24).
	Constraint	Regulations, compliance and ethical enforcement activities in many organizations have been confined to a few specific operating silos such as HR, corporate security, and legal, and have been conducted either on paper or on spreadsheets, making the procedures difficult to share across the organization (41).	Data Protection Act, Chartered Institute of Personnel Development, and Information Commissioner’s Office provide extensive guidance on responsibilities and obligations of employers (53).	For many multinationals the cost of administering a pan-European HR & Payroll policy, with all the complexities of EU and country reporting regulations and compensation and benefits variations, has made the HR & payroll outsourcing message a compelling one (68).	Severe integration issues exist when grocers try to implement fined tuned HCM systems to support the blueprint as in the case of Brookshire Grocery store (25)

Blueprint	Force	Japan	UK	Spain	US
Supply Management Fast & Responsive Service	Driver	The smaller supermarket chains around the country are slowly being acquired. Larger retailers are consolidating and expanding their territories. Retailers have too much inventories on hand and they need to cut their cost structure, both in terms of labor costs and store operations, and reducing inventories (42).	Tesco uses RFID tags on milk and DVDs to track product from production facilities to shelf. Supplies can be replenished faster and DVD stocks better organized on shelf (54).	Grocery retailers such as Asda and Tesco have been marked to increase their product ranges, however European companies are still refining the use (CPFER, 3PL, 4PL) infrastructure to stave off the competitive pressures of expansion (69).	Early adoption of EDI as common standardized means of integrated SCM systems communication has smoothened the efforts of SCM integrations between grocery stores and suppliers (26).
	Constraint	focused (42).	Shorter order times, faster payment, interaction by tech, finance and stock management personnel (55).	ERP does not as of yet have a dominant industry association such as Manufacturing Execution Systems Association (MESA) governing its development (70).	The main driver that contributes to alignment of IT towards SCM blueprint is the strive towards service differentiation and the need to remain competitive by being innovative in the SCM arena (27).
Product Innovation Product Innovation	Driver	Health is a key factor in determining customers’ food choices. It is recognized that the consumption of certain foods can promote improved health and well-being and the prevention or minimization of disease. The addition of functional ingredients enables a product to be distanced from other products within the same category, increasing the profit margins and reducing the impact of price wars with competing commodity products (43).	Introducing private label goods as a way to provide low-cost alternative produces conflict with preferred suppliers (56).	CAD application resulted in an explosion of digital data. Because those design applications created many digital files, it became increasingly difficult to effectively capture, manage, and control the output of those systems (71).	One of the major drivers that support product innovation blueprint usage in US grocery industry is the strive for brand focus, which is implicitly offered by the underlying PLM solution (28).
	Constraint	According to GNX’s VP of Product Development, the typical retailer private brand program comprising several thousand products (SKUs) that are constantly changing, and data maintenance can be a significant challenge. Failure to effectively manage this data can negatively impact consumer confidence and market competitiveness (44).	Grocery stores can provide aggregated customer data to reveal preferences, but manufacturers must make independent decision to change how they produce their goods.	By the 1990’s, industry demanded more sophisticated applications to address issues such as product structure, change control, configuration management, and others (71).	Full collaboration with suppliers has been one of the major issues in ensuring complete strategic alignment with the blueprint (28).

 

Countries Position in Blueprint Evolution

 

Summaries, Interpretations, and Lessons Learned

 

•Customer’s are time-consciousness and demand power to establish preferences and satisfaction level has an ever increasing influence on the development and acceptance of multi-channel blueprints.

•Spend management is an essential dimension in business intelligence solutions, enabling better visibility into factors influencing strategic decisions.

•Leading grocery companies have invested enormous time and capital into aligning IT and business processes by standardizing applications.

•Retailers in the grocery industry search for innovative and efficient ways to integrate and standardize supply chain management by leveraging available IT resources to reinforce their business processes.

Conclusion

 

•Technology and innovation infusion has a positive impact on companies to quickly devise methods to establish effective means to perform sales promotions, improve customer service, provide easier and efficient tracking of products and supply chain management, and cut across multiple channels.

 •Leading grocery retailers are distinguished by their significant attention to—and investment in—aligning people, processes and technology.

 •To gain competitive advantage, retailers, manufacturers and wholesalers look for ways to reduce costs and improve response time by improving and standardizing their business processes.

•The major influences in the usage of product innovation blueprint are brand focus & subsequent differentiation and the strive towards effective means of product life cycle visualizations and subsequent IT alignment in satisfying a powerful customer.

References

 

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2. Martinez S., Kaufman P. (n.d.) Twenty Years of Competition Reshape the U.S. Food Marketing System. Retrieved on February 14, 2010 from http://www.ers.usda.gov/AmberWaves/April08/Features/FoodMarketing.htm.

3. SAS.com. (2008). SAS® Solutions for the grocery industry. Retrieved on February 14, 2010 from

  http://www.sas.com/resources/brochure/sas-solutions-for-grocery-industry-overview.pdf.

4. Carlo J. (2009). Supermarket Pharmacy Trends. Retrieved on February 12, 2010 from

  http://www.gmdc.org/assets/pdf/hbw09_business_session_supermarket_pharmacy_trends.pdf.

5. PollackAssociates (2002). Supermarket Technology. Retrieved on February 12, 2010 from

  http://www.supermarketalert.com/pdf%20docs/2Techology.pdf.

6. Innovation America (2009). New Model of Governance of American Innovation. Retrieved on April 15th, from http://www.innovationamerica.us/index.php/inthenews/bendis-ia-in-the-news/1191-new-model-of-governance-of-american-Innovation.

7. Neilson News. (2009). New Product Innovation In A Recession: More Challenges, But Opportunities Remain. Retrieved on April15th from

  http://blog.nielsen.com/nielsenwire/consumer/new-product-innovation-in-a-recession-more-challenges-but-opportunities-Remain/

8. cspa.org (2010). Industry Calls on Congress for Stakeholder Process to Modernize Outdated Chemical Law. Retrieved on April 15th, from http://www.cspa.org/infocenter/2010/03/industry-calls-on-congress-for-stakeholder-process-to-modernize-outdated-chemical-law/.

9. Mangaraj S. and Senauer B. (2008). A segmentation analysis of US grocery stores.  Retrieved on February 12, 2010 from

  http://ageconsearch.umn.edu/bitstream/14328/1/tr01-08.pdf

10. Imlay T. (May, 2006). Challenges in Today’s U.S. Supermarket Industry. Retrieved on February 12, 2010 from

. Martinez S., Kaufman P. (n.d.) Twenty Years of Competition Reshape the U.S. Food Marketing System. Retrieved on February 14, 20 from   http://www.ers.usda.gov/AmberWaves/April08/Features/FoodMarketing.htm

12. Kaarst-Brown M.L. (2005). Understanding an organization’s view of the CIO: The role of assumptions about IT. MIS Quarterly Executive

  Vol. 4 No. 2 / June 2005

13. Pearlson K. E., Saunders C.S. (2008). Managing and using information systems. John Wiley & Sons Inc.

14. Kalakota R., Robinson M. (2003). services Blueprint – Road map for execution. Addison Wesly

15. Puciarelli J.C. (n.d.) Coping with the ” New Normal” ― How the Changed Economy Is Shaping IT Practices. on March 5th from 

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16. Andreine, D. (2008, October). Multi-Channel Integration Strategies and Environmental Aspects: A Conceptual Framework In Retailing.

  Retrieved on 25th Feb, from http://www.gcbe.us/8th_GCBE/data/Daniela%20Andreini.doc

17. sas.com (n.d.). SAS solutions for Grocery Industry. Retrieved on 25 February 2010 from http://www.sas.com/resources/brochure/sas-solutions-for-grocery-industry-overview.pdf.

18. Zahey, D.L. (n.d.). Challenges and Solutions in Multi Channel Retailing. Retrieved on 25 February 2010 from   http://www.junctionsolutions.com/programs/Challenges.pdf.

19. Carlo J. (2009). Supermarket Pharmacy Trends. Retrieved on February 12, 2010 from   http://www.gmdc.org/assets/pdf/hbw09_business_session_supermarket_pharmacy_trends.pdf.

20. Slovenia, B. (2004, June). Developing a framework for multi-channel strategies – An analysis of cases from the Grocery Retail Industry. Retrieved   on 25th Feb, from http://www.docstoc.com/docs/2365947/Developing-A-Framework-For-Multi-Channel-Strategies-%EF%BF%BD-An-  Analysis

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. Gottlieb M. S. (2006). Grocery Stores- An Industry Study. Retrieved on February 12, 2010 from http://www.msgcpa.com/files/Grocery.pdf.

 

2. Martinez S., Kaufman P. (n.d.) Twenty Years of Competition Reshape the U.S. Food Marketing System. Retrieved on February 14, 2010 from http://www.ers.usda.gov/AmberWaves/April08/Features/FoodMarketing.htm.

 

3. SAS.com. (2008). SAS® Solutions for the grocery industry. Retrieved on February 14, 2010 from

  http://www.sas.com/resources/brochure/sas-solutions-for-grocery-industry-overview.pdf.

 

4. Carlo J. (2009). Supermarket Pharmacy Trends. Retrieved on February 12, 2010 from

  http://www.gmdc.org/assets/pdf/hbw09_business_session_supermarket_pharmacy_trends.pdf.

 

5. PollackAssociates (2002). Supermarket Technology. Retrieved on February 12, 2010 from

  http://www.supermarketalert.com/pdf%20docs/2Techology.pdf.

 

6. Innovation America (2009). New Model of Governance of American Innovation. Retrieved on April 15th, from http://www.innovationamerica.us/index.php/inthenews/bendis-ia-in-the-news/1191-new-model-of-governance-of-american-Innovation.

 

7. Neilson News. (2009). New Product Innovation In A Recession: More Challenges, But Opportunities Remain. Retrieved on April15th from

  http://blog.nielsen.com/nielsenwire/consumer/new-product-innovation-in-a-recession-more-challenges-but-opportunities-Remain/

 

8. cspa.org (2010). Industry Calls on Congress for Stakeholder Process to Modernize Outdated Chemical Law. Retrieved on April 15th, from http://www.cspa.org/infocenter/2010/03/industry-calls-on-congress-for-stakeholder-process-to-modernize-outdated-chemical-law/.

 

9. Mangaraj S. and Senauer B. (2008). A segmentation analysis of US grocery stores.  Retrieved on February 12, 2010 from

  http://ageconsearch.umn.edu/bitstream/14328/1/tr01-08.pdf

 

10. Imlay T. (May, 2006). Challenges in Today’s U.S. Supermarket Industry. Retrieved on February 12, 2010 from http://msdn.microsoft.com/en-us/library/aa479076.aspx

. Martinez S., Kaufman P. (n.d.) Twenty Years of Competition Reshape the U.S. Food Marketing System. Retrieved on February 14, 20 from   http://www.ers.usda.gov/AmberWaves/April08/Features/FoodMarketing.htm

12. Kaarst-Brown M.L. (2005). Understanding an organization’s view of the CIO: The role of assumptions about IT. MIS Quarterly Executive

  Vol. 4 No. 2 / June 2005

13. Pearlson K. E., Saunders C.S. (2008). Managing and using information systems. John Wiley & Sons Inc.

14. Kalakota R., Robinson M. (2003). services Blueprint – Road map for execution. Addison Wesly

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  http://www.ariba.com/resourcelibrary/ content/assets/newnormal.pdf

16. Andreine, D. (2008, October). Multi-Channel Integration Strategies and Environmental Aspects: A Conceptual Framework In Retailing.

  Retrieved on 25th Feb, from http://www.gcbe.us/8th_GCBE/data/Daniela%20Andreini.doc

17. sas.com (n.d.). SAS solutions for Grocery Industry. Retrieved on 25 February 2010 from http://www.sas.com/resources/brochure/sas-solutions-for-grocery-industry-overview.pdf.

18. Zahey, D.L. (n.d.). Challenges and Solutions in Multi Channel Retailing. Retrieved on 25 February 2010 from   http://www.junctionsolutions.com/programs/Challenges.pdf.

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. Lavazza Selects ATG to Consolidate Multiple Sites Into Unified, Global Portal; ATG Enterprise Portal Suite Unites New B2B, B2C, and B2E

Initiatives. Business Wire. 2002. HighBeam Research. Retrieved on 27 February 2010 from http://www.highbeam.com.

67. Watson, Elaine. “The hub lines: B2B exchanges must make strides in supply chain services if they’re to realise their grand designs. (clicks not

bricks).” Grocer. William Reed Ltd. 2002. HighBeam Research. 8 Mar. 2010 http://www.highbeam.com

68. ARINSO International to focus on Managed Payroll Services as part of European outsourcing strategy; Conor Gallagher joins from LogicaCMG to

lead initiative.” M2 Presswire. M2 Communications Ltd. 2003. Retrieved April 01, 2010 from HighBeam

Research:  http://www.highbeam.com/doc/1G1-104875127.html

69. Boom time for outsourced logistics business; Enterprise expenditure on third-party logistics providers set to increase significantly. M2 Presswire.

M2 Communications Ltd. 2006. Retrieved March 27, 2010 from HighBeam Research: http://www.highbeam.com/doc/1G1-141005595.html

70. Rondeau, Patrick J.; Lewis A. Litteral. Evolution of manufacturing planning and control systems: from reorder point to enterprise resources

planning. Production & Inventory Management Journal. American Production and Inventory Control Society Inc. 2001.

Retrieved March 27, 2010 from HighBeam Research: http://www.highbeam.com/doc/1G1-83045565.html

71. CIMData (2003). PDM to PLM: Growth of An Industry. Retrieved on April 15, 2010 from

http://www.e-nea.com/servicios/documentacion/PDM%20to%20PLM%20-%20Growth%20of%20An%20Industry%20-

%20March%202003.pdf.

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hhttp://www.santella.com/frequent.htm#SUPERMARKET%20FACTS%20-%20INDUSTRY%20OVERVIEW%2020

73. Goodman A. (1996). New York Times. International Business. Small Family-Run Stores in Spain Are Fighting to Limit the Hypermarkets.

Retrieved on 27 February 2010 from http://www.nytimes.com/1996/01/06/business/international-business-small-family-run-stores-spain-are-

fighting-limit.html

Strategic alignment of IT resources – A case study in Grocery industry (Part 1)

Strategic Alignment of IT Resources

Grocery Industry

 

Introduction

 Grocery Industry Introduction

• NAICS 445110: Supermarkets and Other Grocery except Convenience Stores . This U.S. industry comprises of establishments generally known as supermarkets and grocery stores primarily engaged in retailing a general line of food, such as canned and frozen foods; fresh fruits and vegetables; and fresh and prepared meats, fish, and poultry.
• Previously , grocery stores dominated their regional markets, however today, they are evolving into the global market at increasing rate.
• The top 15 global supermarket companies account for more 30% of the world supermarket sales (72).

Sales in billions

 

Global & Regional Players

 

Industry & Firm Characteristics

style='width:100.0%;border-collapse:collapse;mso-yfti-tbllook:1184;mso-padding-alt:
0in 0in 0in 0in'>

	Japan	UK	Spain	US
Industry Size	$370 billion (29)	$185.6 billion (45)	$78 billion (57)	$820 billion (1)
General Competitive Landscape style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>	No nation-wide supermarket chains. Increasing number of largest regional supermarkets compete directly with convenience stores and they are dwarfed by the likes of 7-Eleven (29). style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>	Growing dominance of large grocery chains prompted Office of Fair Trading to review competitive practices of largest retailers. class=GramE>Large chains exploits customer databases to provide customized coupons and discounts (46).	Fragmented & expensive logistics, and lack of centralized distribution. No strong competition from other imported products. Products not always priced competitively. Short shelf-live products can be problematic due to time & resources for new/unknown markets (57). style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>	Since Wal-Mart has evolved to be the most competing player, their expansion led to close at least 2000 supermarkets. Most pressing issue for small and mid-sized grocers is to keep costs low in order to compete with hypermarts, as new growth opportunities are few.
Improved Marketing Strategies style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>	Marketing to average Japanese firm is not a priority. To succeed in Japan, they concentrate on production quality and low prices (30).	Large chains provide customized coupons and discounts and websites offers online ordering and home delivery service. Customers able to view many products online (46). style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>	Largest grocery stores provides club card that gives discounts and loyalty to customers. Attract more customers by advertising via radio, local newspaper and national television (58).	Strategies focus on standardized promotions, personalized customer interactions and maximizing ROI (2, 3). style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>
Improved Customer Experience by Usage of Research and Technology	Use SMART systems to capture customers’ demands and improve inventory procedures (31).	Big Four make extensive use of online presence for e-mail marketing, recruiting, reward point checker, and surveys. Significant effort spent trying to increase online activity without hurting in-store sales. Growing recognition by retailers that web experience must be coordinated with traditional retail channels (47). style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>	Mining consumer data to unearth new opportunities to provide better customer service (59). style='font-size:18.0pt;font-family:"Arial","sans-serif";mso-fareast-font-family: "Times New Roman"'>	Use specialized software, programs for store management and RFID technology.

What limitations should be imposed on datamining of email traffic patterns?

E-mail traffic is one of the main target of intelligence surveillance to detect terrorist and other malicious activities. Its rather easy and result oriented when compared to wiretapping of other data streams. This is because mails send or received can be easily linked to a chain that it belongs to. It can be used to identify the community that the email belong to. I would personally suggest unlimited right to analyze any suspicious traffic identified by pattern analysis. Various accepted methods are being experimented to effect this.

Rather than directly wiretapping and analyzing the content of individual mail in detail, the suggested method is to “look for the critical links that form bridges or betweenness of separate groups” (Muir, 2003). This would bring out a group of people communicating stuff that can include terrorist activities. Suggested method is to use automated pattern analysis to detect for suspicious communication and if any such is identified, intelligence force may use CALEA to further take actions.

Here’s a link to ‘Process Mining’ that introduces a new method of result oriented data-mining to uncover social networks from e-mail traffic. The method works on event logs created by e-mail clients and tries to uncover social relationships that connects people, potentially applicable to trace terrorist groups.

Process mining as applied to email-traffic is to -

1. Create event logs out of email (subject, To-from ids, send/received dates, mail headers etc) such as those handled by MS Outlook, usually dumped into a database.

2. Use the so called ProM framework to mine the event log to uncover social relationships.

It is also true that there should be limitations applied to data mining that will not search for specific content in an email, as there are privacy concerns attached to it. All data mining techniques are to be “privacy-preserving”. Here’s a nice article – Privacy preserving data mining -, in which they outline the current state of this procedure that could be effectively utilized for a controlled data mining in intelligence surveillance, including e-mail traffic.

Article copyright (c) 2010 – 2020 – Deepesh Joseph (deepeshjoseph@yahoo.com)
Get all articles from www.getallarticles.com. Be informed and gain knowledge. Good resource for research and reviews.

Current usage and future of XML Database Management Systems

Deepesh Joseph
April, 2009

Purpose

The purpose of this document is to present a management report that analyzes the current usage of XML based Database Management System, its impacts and future trends of usage.

Present Landscape

Database Management Systems (DBMS) has been constantly evolving ever since first DBMS was installed and used based on Codd’s relational concepts. We saw steady pace of innovations in DBMS technology and usage starting from Hierarchical databases to Object/Relational databases and from centralized to decentralized database systems. DBMS evolution has also been supported by innovative ways of collecting, storing, processing and retrieving data such as from basic genealogy to complex forensic data (Hoffer J.A). These innovations shows how closely linked is DBMS to the advancements in general technology landscape, overall systems architecture and type and nature of Information Management (IM). For example, Object Oriented systems development has supported the development of Object Oriented DBMS. Similarly, distributed systems architecture has lead to distributed DBMS setup. Also, advanced needs of managing complex information has lead to the development of various sophisticated spatial/clinical/genetic databases.

The current landscape of DBMS is a collection of above mentioned trends that has continued to evolve in the past 30 years. Different data management domains calls for specific technology based DBMS viz. Relational versus Object Oriented. Again within each specialized DBMS technology domain, their exists number of vendor products that compete to provide efficient database management. For example, Oracle and DB2 compete each other in Relational database domain. Competition is also prevalent in the way the DBMS product license is offered such as with MySQL, PostgreSQL (IdeaByte) and recently with Sybase (Product: Sybase ASE 15 Express) when it announced to offer it free on Linux (peterdobler.com).

General Overview

XML (Extensible Markup Language, developed by W3C) based DBMS is one such innovative usage of DBMS prompted by pervasive usage of web based database applications and its related need of managing frequent storage and retrieval of not-very structured data in document format (i.e. as web pages). This need goes in line with what was described in the above section as related to evolving and specific IM needs. XML, in its basic sense of existence, is used to create, store and transport either data-centric (such as a SOAP request and response) or document-centric data (such as XHTML documents). In either case, XML provides an ordered way to arrange data in cascaded data tags which could be easily read and processed using XML query language (eg: XQuery). Even though XML was originally designed to create XHTML (Extensible HTML) documents, technologists and database vendors realized the importance of XML in storing and retrieving semi-structured data, efficiently (Obasanjo, D).

Strictly speaking, XML is not a database, but an efficient medium to represent and transport data across multiple systems. Also XML is not a DBMS in strict sense, but could provide some basic features of DBMS via XML documents, XML query languages, programming interfaces etc (Bourret, R.), i.e use XML documents to store data (eg: DTDs) which is queried and accessed by XML query language (eg: XQuery). This is the most basic model of XML DBMS and forms the basis of all modern Native XML DBMS available such as Sedna. Sedna is a DBMS that supports some traditional DBMS features such as update and query languages, query optimization, fine-grain concurrency control, various indexing techniques, recovery and security. Sedna also supports W3C based XQuery language which could be used to conduct complex data management operations such as XML data querying, XML data transformations and even business logic computation (ispras.ru). Another type of XML DBMS is the normal DBMS such as DB2 or Oracle that provides support for XML based data storage and retrieval through special storage and data management features. For examples, latest releases of Oracle provides native XML data type which could be used to store XML data. DB2 provides support for XQuery based data management where data could be exported/imported into the database in XML format.

As we saw from above analysis, XML DBMS’s core usage is based on the need to handle vast amount of document centric or XHTML centric data. This is the basic feature that distinguishes XML DBMS from current DBMS technology. If we have a database application that is web based and it requires heavy processing of documents/objects (storage/access/search of web pages, music/video files, directory/phone book type of data etc) and that requirements of structured document/data storage is not very relevant, then we could potentially reap benefits by using a native XML DBMS. Where as, if we plan to implement a heavy transaction oriented web application which involves atomic transactions, such as bank transactions, we should be using a traditional DBMS such as DB2 or Oracle (provided these support native XML transactions).

Impact and Future Directions

Degree of disruption:

XML DBMS has not bought any level disruption to the current DBMS market or its usage. It has been developed and used as an add on tool to support a specific IM need, mainly in web based database applications. The name ‘XML DBMS’ sounds like a misnomer since XML or native XML DBMS does not provide all features of a full blown DBMS. Since XML and its query language confirms to W3C standards, it is could be easily integrated with all popular relational/ object oriented DBMS as add-on feature.

Costs:

Costs associated with implementing XML DBMS depends on the type of solution that is sought for. If we plan to use native XML DBMS, most of it is free/open source, which brings down total cost of ownership to zero. Most of the present day DBMS such as Oracle, DB2 etc comes with native XML support, so that no extra cost is incurred if we are already using one of these DBMS.

Benefits:

XMLS DBMS provides maximum benefit when used for driving heavy document-centric web applications as we saw in ‘General Overview’ section. XML DBMS provides most cost effective way to store and process document data since very little effort is required to present user data since the underlying data format for the transport and presentation layers are in the same, i.e XML or a derivative such as XHTML.

IT infrastructure changes:

Since XML DBMS is used to support the strategy of building cost effective XML data management, most of the supporting system architecture would be already in place – such as XML documents (that follows a specific XML schema) , XML parser/extractor and Query tool (which is supported by almost all of the web scripting languages and native XML DBMSs) and native XML support by the underlying relational DBMS. For example, if we plan to implement XML DBMS for a web based application which is LAMP (Linux/Apache/MySQL/PHP) based, all supporting technology (DTD/XML schema support, XQuery/XPath based query language support, SAX/DOM based programming interface support, native XML support within MySQL etc ) is inherent to the underlying technology infrastructure. The most critical factor that drives selection of XML DBMS is thus the specific need to support XML centric application architecture.

Skills required:

The basic skill required to implement and manage XML DBMS is knowledge of XML, XML schema, XML query language, XML parsers/extractors, XML programming interfaces, usage of native XML functions (if using relational based DBMS) and knowledge of native XML DBMS (if native XML DBMS such as Sedna drives the database application).

Future directions in usage of XML DBMS:

XML technology in general is being widely accepted as a standard medium of data transport between disparate systems. The standards are W3C complaint and XML query tools and APIs are constantly improved to be interoperable with wide range of relational, object-oriented and non-relational databases,. This scenario supports the wide acceptance of XML based databases for powering systems which are less web centric in nature. To show the immense possibility of effectively utilizing the power of XML DBMS, provided is a sample system as shown below (infolab.cs.unipi.gr). The system leverages XML DBMS technology to build and manage a Pattern Base Management System (PBMS) which enables user to store and retrieve patterns, just like data.

Figure 1. XML DBMS based PBMS (Image courtesy – http://infolab.cs.unipi.gr/projects/pbms_description/pbms_site_v5b.htm)

The idea of using XML based DBMS originated with the concept that patterns are “compact and rich semantic representations of data”, which could be effectively represented in XML schema. The figure shows how data is extracted from data sources via XML and further fed into underlying relational based (or a native XML DBMS) through appropriate XML query tool (in this case, it is a Pattern Definition/Query/Manipulation Language or PD/Q/ML).

Glossary

XML – Extensible Markup Language, developed by World wide web consortium (W3C) to deal with shortcoming of HTML.
IM – Information Management
SOAP – Simple Object Access Protocol used as a medium to communicate between two systems, eg: an application and a web service.
Native XML DBMS – Pure XML based DBMS without underlying relational or any other traditional DBMS support
XQuery – An XML query tool
XHTML – Extensible HTML
SAX – Simplae API for XML
DOM – Document Object Model
DTD – Document Type Definition

Sources

1. Hoffer J.A et.al (March 20, 2006). Modern Database Management. Prentice Hall 8th edition.

2. lib.bioinfo.pl. (2009). Database Management System trends – Retrieved April 18, 2009 from http://lib.bioinfo.pl/meid:3642

3. IdeaByte. (February 13, 2003). IT Trends 2003: Database Management Systems – Retrieved April 18, 2009 from http://www.quest.com/industry_coverage/pdfs/2003db_trends.pdf

4. peterdobler.com. (January, 2009). Database Technology Trends Behind The Scenes of Database Evolution -Sybase ASE 15 for Linux – FREE?- Retrieved April 19, 2009 from http://www.peterdobler.com/

5. Feuerlicht G. (n.d.). Recent Trends in Database Technology. Retrieved April 19, 2009 from http://www-staff.it.uts.edu.au/~chin/dbms/dbtech.htm

6. Bourret R. (September, 2005). XML and Databases. Retrieved April 19, 2009 from http://www.rpbourret.com/xml/XMLAndDatabases.htm

7. Obasanjo, D. (n.d.). An Exploration of XML in DBMS. Retrieved April 19, 2009 from http://www.25hoursaday.com/StoringAndQueryingXML.html

8. ispras.ru (2009). About Sedna (XML DBMS). Retrieved April 20, 2009 from http://modis.ispras.ru/sedna/

9. infolab.cs.unipi.gr. (n.d.). XML-based Pattern Base Management system. Retrieved April 19, 2009 from http://infolab.cs.unipi.gr/projects/pbms_description/pbms_site_v5b.htm

10. Jiang H. et.al (n.d.). XParent: An Efficient RDBMS-Based XML Database System. Retrieved April 19, 2009 from http://csdl.computer.org/comp/proceedings/icde/2002/1531/00/15310335.pdf

11. Zhou A. et.al. (n.d.). VXMLR: A Visual XML-Relational Database System. Retrieved April 20, 2009 from http://www.dia.uniroma3.it/~vldbproc/102_719.pdf

(c) Deepesh Joseph, 2001

Compare and contrast data warehouse principles

Operational Vs Informational system : Operational Systems are those which are based on current data and thus supports current/day-to-day functioning of a business. Informational Systems on the other hand are based on historical information and thus supports complex data mining/analysis and decision making. The core difference lies in the way queries are run to process/analyze data. Simple, planned and real time queries are run on an operational system that acts on small data sets, where as in an informational system, huge complex queries are executed that acts on bulk amounts of data sets.

Operational database vs. Data warehouse : Operational database is the database that we use to implement an operational system and Data warehouse is one of the opted database design that we use to implement an information system. Operational database is less complex in design (such as relational based and in 3rd normal form) supporting storage and processing of current/operational data where as Data warehouses are designed to support effective storage and processing of large volumes of historical data. Operational databases are relational in most cases, where as Data warehouse follows database models such as star or snowflake schema (multidimensional data models).

Data warehouse vs. Data mart : Data warehouse comprises of a complete domain of data pertaining to an enterprise, where data marts are scaled down version of a data warehouse that is confined to a particular data domain (eg: sales). In other words, data marts are a subset of data warehouse.

OLTP vs. OLAP : OLTP or Online transaction Processing Systems refers to those Information Management systems that are based on operational databases and thus supports current business transactions such as sales order processing. OLAP or Online Analytical Processing systems are those which are based on data warehouses or similar solutions that are designed to support ad-hoc query analysis or data-mining based on historical data. OLTP supports current business functions and flows where OLAP supports decision making based on complex data analysis.

Ad hoc queries vs. Data Mining : Ad hoc queries are queries which are designed for a specific known purpose and cannot be dynamically altered to suit a different need. Data mining on the other hand allows us to create dynamic queries based on real time user/process inputs and thus produce data patterns which are not originally known to the business.

Star schema vs. Snow flake schema : Both Star and Snow flake schemas are data warehouse database design models that supports multidimensional data modeling. Star schema is the most simple data warehouse data model which consists of one or more fact tables in the middle and many dimensional tables connected to the fact table, thus creating the shape of a star. Snowflake data warehouse data model are similar to star schema, except that the dimensional tables are normalized. Snowflake schema is usually used when we convert an already normalized transactional database into a data warehouse, where all dimensional tables would be already normalized.

(c) Deepesh Joseph, 2001
Related websites — http://www.getallarticles.com

Compare and contrast distributed database principles

a. Homogeneous vs. heterogeneous distributed database system

Homogeneous database systems involve similar databases distributed over the network (on separate machines). Example of homogeneous database system is an enterprise’s nation-wide ERP system which comprises of distributed databases, all of which are Oracle. Heterogeneous database systems on the other hand are distributed database systems that consist of at least one different database. Example of heterogeneous database system is an enterprise wide intranet application which consists of databases such as MS SQL server and DB2, which belong to the same integrated database application.

b. Autonomous vs. Non-autonomous distributed database system

Autonomous and Non-autonomous distributed database is a sub-set of Homogeneous databases. Autonomous distributed database are independent databases (separate data residing in each database) that function independently, but, are integrated by the controlling application software. Non-autonomous distributed database are homogeneous databases where data is distributed across homogeneous nodes and is controlled by DBMS at each node. Example for a autonomous distributed database system is Oracle based data marts which manages data pertaining to sales, distribution and inventory. Example for a non-autonomous distributed database system is Oracle based global sales database which is partitioned across multiple databases.

c. Federated vs. Unfederated

Federated database systems are collection of heterogeneous database systems which is integrated together to function as a single system. Each constituent database system is autonomous and control can be exercised to each local database component of the federation. Unfederated database systems are collection of homogeneous database systems which are generally non-autonomous by nature and employs centralized control. Example for a federated database system would be an extended heterogeneous distributed database system that span across multiple database vendors and multiple enterprise departments. Example for unfederated database system would be an extended homogeneous distributed database system that spans across a global enterprise function.

(c) Deepesh Joseph, 2001
Related websites — http://www.getallarticles.com

Pros and Cons of normalizing data into 3NF form

Deepesh Joseph
February 2009

The main purpose of normalization is to reduce data redundancy and avoid inconsistent data. Normalization leads to separation of unrelated entities into separate entities. In effect, normalization leads to clean database design. Since we do not store redundant data, we save storage space and save resources to maintain (update, delete) redundant data.

But, there are instances where we do not need to fully normalize data. The example provided in question is an excellent example to explain why do we allow de-normalized data. Looking at the customer address data, it is desirable to design city, state, country and postal codes as separate entities since they could be represented by thier own unique identifiers (state_id, country_id, zip_id) and that multiple customers may belong to same country, state, city and zip. Suppose we did design these as separate entities and try to retrieve data for the following problem –

“Generate report of all customer belonging to ‘US’ and who reside in ‘FLORIDA’s ‘TAMPA’ city in ‘33601′ postal code.”

The query would be something like –

“SELECT
c.customer_first_name, c.customer_middle_name, c.customer_last_name
FROM
customer c, customer_address ca, address_city act, address_zip az, address_state as, adress_country ac
WHERE
c.customer_id=ca.customer_id and c.city_id=act.city_id AND c.zip_id=az.zip_id
AND c.state_id = as.state_id and c.country_id = ac.country_id AND ac.country_name = ‘US’
AND as.state_name = ‘FLORIDA’ AND act.city_name = ‘TAMPA’ AND az.zip_code = ‘33601′”
Notice the joins (c.customer_id=ca.customer_id and c.city_id=act.city_id etc) required in the SQL to retrieve the required information. SQLs joins are considered to be very expensive when there is huge amount of data, say we have a tera byte of data within customer table. The four additional joins is going to be very expensive and will lead to unacceptable system response time.

If we de-normalize data and allow country, state, city and zip data to reside within customer_address table, then we could rewrite the above query as –

“SELECT
c.customer_first_name, c.customer_middle_name, c.customer_last_name
FROM
customer c, customer_address ca
WHERE
c.customer_id=ca.customer_id AND ca.country_name = ‘US’
AND ca.state_name = ‘FLORIDA’ AND ca.city_name = ‘TAMPA’ AND ca.zip_code = ‘33601′”

After de-normalization, the query would run much faster. So, in effect, normalizing data into 3NF form is not always practical.

(c) Deepesh Joseph, 2001
Related websites — http://www.getallarticles.com