Why Patterns of Data Modeling?
I published another chapter of the new book on “Patterns of Data Modeling” of
Dr. Michael Blaha. All together you can now download three chapters of the book:
Tree Template, Models, and Universal Antipatterns.
At the same time, I asked Dr. Blaha a few questions.
At the end of the interview you`ll find some more opinions on this topic.
Q1. What are Patterns of Data Modeling?
Michael Blaha: Experienced data modelers don’t limit their thinking to primitive constructs. Rather they leverage what they have seen before. Patterns of data modeling are ways of cataloging past superstructures that are profound and likely to recur.
There are different aspects of data modeling patterns. There are models of common data structures (mathematical templates), models to be avoided (antipatterns), core concepts that transcend application domains (archetypes), and models of common services (canonical models). Modelers should avail themselves of the full pattern toolkit and not focus on one technique to the exclusion of others.
The literature covers abstract programming patterns that exist apart from application concepts. For example, the gang of four book — “Design Patterns: Elements of Reusable Object-Oriented Software” has excellent coverage of abstract programming patterns. There is no reason why databases should not have a comparable level of treatment. Until my recent book (“Patterns of Data Modeling“) the literature has lacked an abstract treatment of data modeling patterns.
Q2. Where and when are Patterns of Data Modeling useful?
Michael Blaha: All experienced modelers should use data modeling patterns. It is important to reuse ideas that have been tried and tested, rather than reinvent technology from scratch. I know that data modeling patterns are useful because this is the way that I think as I perform my work as an industrial consultant.
I use data modeling patterns for application data models, enterprise data models, data reverse engineering, and abstract conceptual thinking. Data modeling patterns are not a panacea to the troubles of development, but they are part of the solution. With patterns, developers can accelerate their thinking and reduce modeling errors.
Q3. Is there any difference in the applicability of Patterns of Data Modeling if the underlying Database System is a relational database as opposed to for example an Object Oriented or a NoSQL database?
Michael Blaha: No. That is the whole premise of software engineering — to quickly address the essential aspects of a problem and defer implementation details. A conceptual data model is focused on finding the important concepts for a problem, delineating scope, and determining the proper level of abstraction. All this deep, early thinking happens regardless of the eventual implementation target. Data modeling patterns mostly apply to the early stages of software development
Bill Premerlani and I took this approach in our 1998 book (“Object-Oriented Modeling and Design for Database Applications”). We presented detailed mapping rules for how to implement conceptual models with relational databases, an object-oriented database (ObjectStore) and flat files. Our 1991 book (“Object-Oriented Modeling and Design”) and its 2005 sequel explained how to map OO models to several programming languages.
So patterns of data modeling (as well as programming patterns and other kinds of patterns) apply regardless of the eventual downstream implementation.
Q4. What’s the difference between a pattern and a seed model?
Michael Blaha: A seed model is specific to a problem domain. It is a tangible piece that you can extend to build an entire application. Several authors (such as Hay, Fowler, and Silverston) have published excellent books with seed models. In constrast, a pattern is abstract and stands apart from any particular application domain. Patterns are at the same level of abstraction as UML classes, associations, and generalizations. A pattern is a composite building block. Seed models and abstract patterns are both valuable techniques. They are complimentary and are often used together.
Q5. What do you see as frontier areas of databases and data modeling?
Michael Blaha: I’m now working on a new topic — SOA and databases. SOA is an acronym for Service-Oriented Architecture, an approach for organizing business functionality into meaningful units of work. Instead of placing logic in application silos, SOA organizes functionality into services that transcend the various departments and fiefdoms of a business. A service is a meaningful unit of business processing. Services communicate by passing data back and forth. Such data is typically expressed in terms of XML. XML combines data with metadata that defines the data’s structure. A second language — XSD (XML Schema Definition) — is often used to specify valid XML data structure.
The promise of SOA is being held back by a lack of rigor with XSD files. Many developers focus on the design of individual services and pay little attention to how the services fit together and collectively evolve. Enterprise data modeling is the solution to this problem. A data model is essential for grasping the entirety of services and abstracting services properly. A data model also provides a guide for combining services in flexible ways.
I see evidence for a lack of data modeling in my consulting practice. I have studied several XSD standards and they all ignore data models. The literature in the area of SOA and data modeling is sparse. The current situation is untenable and SOA projects must pay more attention to data.
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“Patterns of data modeling are very important. They enable data modeling efforts to be both effective and efficient. Working without patterns is like wandering around in the data wilderness trying to find your way.
SOA and Data. This is another vital area that must be addressed. I am doing it in my practice. It brings together data, metadata, metacards, data registries, data catalogs — and service. Very important for scalablility when the data network size grows (e.g., the government, nationwide health services, etc.).” — James Odell.
“I am mostly an object modeller, but I always recommend that my clients start with existing data model patterns rather than with a blank sheet of paper.
The data modelling patterns I most turn to are David C. Hay (Data Model Patterns: Conventions of Thought etc.).” — Jim Arlow.
“I agree with all that Dr. Blaha said advocating the use of patterns. This was very articulately worded, and I like to see those views spread around.
I also recognize that what he’s tried to do in this book is very different from what Len Silverston, Martin Fowler and I did.
It is true that we were focused on modeling the real world–“domains” as he described it. He, on the other hand has abstracted modeling to the point that he describes modeling itself–“tree” structures, undirected graphs, directed graphs, and so forth.
It is true that Dr. Blaha’s book is abstract in the extreme.
In fact, in my new book, Enterprise Model Patterns: Describing the World take on the issue of level of abstraction directly. In this, I am presenting a semantic model that I claim describes the entire enterprise, but on multiple levels of abstraction.
The first (Level 1) is a generic model that any company or government agency can take on as a starting point. It is generic because most attributes are actually captured as data in CHARACTERISTIC entities. (This corresponds to Dr. Blaha’s discussion of soft-coded values.) Thus, they become the problem of the user community, not the data modeler. The data modeler can address the true structures of the business. Yes, this model is organized in terms of five fundamental domains: people and organizations (who), geographic locations (where), physical assets (what), and activities and events (how). It also addresses time (when), but that’s a different kind of model. (This model is based on some 20+ years experience in the field, but I was inspired to write it from my experience over the last few years with the Federal Data Architecture Subcommittee. The committee hasn’t been very effective at creating patterns to distribute to Federal agencies, but it did inspire me to try to capture my views on the subject.)
I then address Level 0, which is a template for the first four categories above. (This is an enhanced version of the THING/THING TYPE model). In addition, at this level are two “meta” models: Document management and accounting. Each of these subject areas itself refers to the entire rest of the model.
At Level 2, I deal with functional specializations. These are more detailed than the level 1 models and make use of the entities in Level 1 combined in specific ways. These subject areas address such things as addresses (both physical addresses–“facilities”–and virtual addresses–telephone numbers, e-mail addresses, etc.), human resources, contracts, and the like. While they are more specialized than level 1, they are still generally applicable patterns. (And these areas address the “why” of the organization.)
At level 3, I address specific industries. For “vertical” models, I take the position that the Level 1/2 models address 80-90% of any company’s requirements. For each industry, however, there are a few special areas that need special attention. These are the things that make that industry unique. I took on a five of these, trying to get a cross-section from completely different worlds: criminal justice, microbiology, banking, oil production, and highway maintenance. If you don’t know anything about one of these industries, here is where you can learn something.
I agree that patterns are technology independent. I disagree that “object” models are technologically independent. That Dr. Blaha began with the gang of four book – “Design Patterns: Elements of Reusable Object-Oriented Software” tells something about his orientation. As it happens, in my latest book, I did (as my colleagues would say) “move over to the dark side”, and use UML as the notation, even though that notation is specifically oriented towards object-oriented design, not business modeling. I had to tweak some of the terms to break out of its object-oriented design history. These are conceptual, business-oriented models, not design models.
In doing this, I may have managed to offend both my data modeling colleagues (“You really have gone over to the dark side, haven’t you?”) and my UML colleagues (“What have you done to my UML?”). Or, perhaps, maybe have started building a bridge between the two groups? Only time will tell.” — Dave Hay.
Video “New and old Data stores”.
You can now freely download the Video of the Keynote Panel “New and old Data stores”, held at ICOODB 2010 Frankfurt on September 29, 2010.
Here is the LINK to download the video.
Since the original file was rather large, I split it into two separate files, each one takes about 6 minutes to download…
The panel discussed the pros and cons of new data stores with respect to classical relational databases.
The panel of experts was composed by:
Ulf Michael (Monty) Widenius, main author of the original version of the open source MySQL database.
Michael Keith, architect at Oracle.
Patrick Linskey, Apache OpenJPA project.
Robert Greene, Chief Strategist Versant.
Leon Guzenda, Chief Technology Officer Objectivity.
Peter Neubauer. COO NeoTechnology.
Moderators were: Alan Dearle, University of St. Andrew, and Roberto V. Zicari, Goethe University Frankfurt.
The panelists engaged in lively discussions addressing a variety of interesting issues, such as: why the recent proliferation of “new data stores”, such as “document stores”, and “nosql databases”; their differences with classic relational databases, how object databases compare with NoSQL databases, scalability and consistency for huge amount of data…to name a few.
RVZ
Proceedings ICOODB 2010 Frankfurt.
The research papers (RESEARCH TRACK) of the ICOODB 2010 Frankfurt conference, have been published by Springer in their Lecture Notes in Computer Science. Here are the details:
Objects and Databases. Dearle, Alan; Zicari, Roberto V. (Eds.)
Proceedings Series: Lecture Notes in Computer Science, Vol. 6348. 1st Edition., 2010, XIV, 161 p., Softcover ISBN: 978-3-642-16091-2
Preface and Table of Contents| September 2010|.
This book constitutes the thoroughly refereed conference proceedings of the Third International Conference on Object Databases, ICOODB 2010, held in Frankfurt/Main, Germany in September 2010.
Most presentations in the Industry Track, Keynotes and Tutorials are available for free download at ODBMS.ORG.
I will very soon upload the video of the very interesting keynote panel “NEW AND OLD DATA STORES” …stay tuned.
RVZ
Presentations of ICOODB Frankfurt 2010.
I have published in ODBMS.ORG most of the industry presentations being given at the ICOODB Frankfurt 2010 conference.
Here are the relevant links:
TUTORIALS:
1. “Object Databases” (PDF 75 pages) by Michael Grossniklaus, Politecnico di Milano. |
2. “Patterns of Data Modeling”(PDF 49 pages) | , by Michael R. Blaha, Modelsoft Consulting Corp.
–>Download Link.
NoSQL Workshop:
1. “Approaches to Data Modeling in Non-Relational Systems Using Apache Cassandra”, by Gary Dusbabek, Rackspace
2. “Dinner in the sky with MongoDB.”, by Marc Boeker, ONchestra.
3. “Scale Out vs. Scale In- a face-off between Cassandra and Redis. ” by Tim Lossen, wooga.
4. “The Graph DB Landscape and SonesDB. “ by Achim Friedland, Sones.
5. “Neo4j for deep spatial and social intelligence. “ by Peter Neubauer, Neo Technology.
6. “Mastering Massive Data Volumes with Hypertable. “ by Doug Judd, Hypertable Inc..
—> Link to Download all presentations (.PDF).
ICOODB KEYNOTES and Industry Track Presentations:
1. “Efficient Development of Event-Driven Systems with Versant Object Database.” by Guenter Ressell-Herbert, Versant
2. “Accelerating Application Development with Objects. “ by Eric Falsken, German Viscuso, Roman Stoffel, db4objects.
3. “The Synergy Between the Object Database, Graph Database, Cloud Computing and NoSQL Paradigms. ” by Leon Guzenda, Objectivity.
4. “Unifying Remote Data, Remote Procedures and Web Services.” KEYNOTE by William Cook, University of Texas at Austin.
5. “Searching the Web of Objects” KEYNOTE by Ricardo Baeza-Yates, VP, Yahoo! Research, Europe and Latin America.
—> Download presentations Link.
5. “State of MariaDB” and “Dynamic Columns in MariaDB“, by Michael (Monty) Widenious, MariaDB.
—> Download link
A lot to read…
Around 200 Researchers from around the world attended the conference
You can see some photos here.
RVZ
The winners of the ODBMS.ORG “Best Object Databases Lecture Notes” Award 2010 are Dr. Michael Grossniklaus and Prof. Moira Norrie, ETH Zürich, Switzerland, for their Lecture Notes “Object-Oriented Databases”.
Second place for:
“Object Database Tutorial”
by Dr. Rick Cattell, Independent Consultant, USA.
Third place for:
“Modern Database Techniques”
by Prof. Martin Hulin, Hochschule Ravensburg-Weingarten, Germany.
The Award Ceremony was held on September 29, 2010, at the 3rd International Conference on Objects and Databases (ICOODB 2010) in Frankfurt.
The Awards recognize the most complete and up to date lecture notes on Object Databases, that have been, or have strong potential to be, instrumental to the teaching of theory and practice in the field of objects and databases. Any Lecture Notes published in ODBMS.ORG during the years 2004-2010 were eligible for the 2010 award.
“This is a very nice recognition to the award winners, and it also encourages others to contribute educational materials that others can use. Very good.” Prof. Alfonso Cardenas, Computer Science Department, UCLA.
One of our expert, Dr. Michael Grossniklaus, has recently been awarded a grant by the Swiss National Science Foundation (SNF) for a fellowship as an advanced researcher in David Maier’s group at Portland State University. There, he will be investigating the use of object database technology for cloud data management.
I asked Michael to elaborate on his research plan and share it with our ODBMS.ORG community.
Q1. People from different fields have slightly different definitions of the term Cloud Computing. What is the common denominator of most of these definitions?
MG: Many of the differences stem from the fact that people use the term Cloud Computing both to denote a vision at the conceptual level and technologies at the implementation level. A nice collection of no less than twenty-one definitions can be found here.
In terms of vision, the common denominator of most definitions is to look at processing power, storage and software as commodities that are readily available from large infrastructures. As a consequence, cloud computing unifies elements of distributed, grid, utility and autonomic computing. The term elastic computing is also often used in this context to describe the ability of cloud computing to cope with bursts or spikes in the demand of resources on an on-demand basis. As for technologies, there is a consensus that cloud computing corresponds to a service-oriented stack that provides computing resources at different levels. Again, there are many variants of cloud computing stacks, but the trend seems to go towards three layers. At the lowest level, Infrastructure-as-a-Service (IaaS) offers resources such as processing power or storage as a service. One level above, Platform-as-a-Service (PaaS) provides development tools to build applications based on the service provider’s API. Finally, on the top-most level, Software-as-a-Service (SaaS) describes the model of deploying applications to clients on demand.
Q2. With the emergence of cloud computing, new data management systems have surfaced. Why?
MG: I see new data management systems such as NoSQL databases and MapReduce systems mainly as a reaction to the way in which cloud computing provides scalability. In cloud computing, more processing power typically translates to more (cheap, shared-nothing) computing nodes, rather than migrating or upgrading to better hardware. Therefore, cloud computing applications need to be parallelizable in order to scale. Both NoSQL and MapReduce advocate simplicity in terms of data models and data processing, in order to provide light-weight and fault-tolerant frameworks that support automatic parallelization and distribution.
In comparison to existing parallel and distributed (relational) databases however, many established data management concepts, such as data independence, declarative query languages, algebraic optimization and transactional data processing, are often omitted. As a consequence, more weight is put on the shoulders of application developers that now face new challenges and responsibilities. Acknowledging the fact that the initial vision was maybe too simple, there is already a trend of extending MapReduce systems with established data management concepts. Yahoo’s PigLatin and Microsoft’s Dryad have introduced a (near-) relational algebra and Facebook’s HIVE supports SQL, to name only a few examples. In this sense, cloud computing has triggered a “reboot” of data management systems by starting from a very simple paradigm and adding classical features back in, whenever they are required.
Q3. What is in your opinion the direction into which cloud computing data management is evolving? What are the main challenges of cloud computing data management?
MG: Data management in cloud computing will take place on a massively parallel and widely distributed scale. Based on these characteristics, several people have argued that cloud data management is more suitable for analytic rather than transactional data processing. Applications that need mostly read-only access to data and perform updates in batch mode are, therefore, expected to profit the most from cloud computing. At the same time, analytical data processing is gaining importance both in industry in terms of market shares and in academia through novel fields of application, such as computational science and e-science. Furthermore, from the classical data management concepts mentioned above, ACID transactions is the notable exception since, so far, nobody has proposed to extend MapReduce systems with transactional data processing. This might be another indication that cloud data management is evolving into the direction of analytical data processing.
At the time of answering these questions, I see three main challenges for data management in cloud computing: massively parallel and widely distributed data storage and processing, integration of novel data processing paradigms as well as the provision of service-based interfaces. The first challenge has been identified many times and is a direct consequence of the very nature of cloud computing. The second challenge is to build a comprehensive data processing platform by integrating novel paradigms with existing database technology. Often cited paradigms include data stream processing systems, service-based data processing or the above-mentioned NoSQL databases and MapReduce systems. Finally, the third challenge is to provide service-based interfaces for this new data processing platform in order to expose the platform itself as a service in the cloud, which is also referred to as “Database-as-a-Service” (DaaS) or “Cloud Data Services”.
Q4. What is the impact of cloud computing on data management research so far?
MG: Most of the challenges mentioned above are already being addressed in some way by the database research community. In particular, parallel and distributed data management is a well-established field of research, which has contributed many results that are strongly related to cloud data management. Research in this area investigates whether and how existing parallel and distributed databases can scale up to the level of parallelism and distribution that is characteristic of cloud computing. While this approach is more “top down”, there is also the “bottom up” approach of starting with an already highly parallel and widely distributed system and extending it with classical database functionality. This second approach has led to the extended MapReduce systems that were mentioned before. While these extended approaches already partially address the second challenge of cloud data management—integrating of novel data processing paradigms—there are also research results that take this integration even further such as HadoopDB and Clustera. The third challenge is being addressed as part of the research on programmability of cloud data services in terms of languages, interfaces and development models.
The impact of cloud computing on data management research is also visible in recent call for papers of both established and emerging workshops and conferences. Furthermore, there are several additional initiatives dedicated to support cloud data management research. For example, the MSR Summer Institute 2010 held at the University of Washington brought together a number of database researcher to discuss the current challenges and opportunities of cloud data services.
Q5. In your opinion, is there a relationship between cloud computing and object database technologies? If yes, please explain.
MG: Yes, there are multiple connections between cloud data management and object database technology which relate to all of the previously mentioned challenges. According to a recent article in Information Week , businesses are likely to split their data management into (transactional) in-house and (analytical) cloud data processing. This requirement corresponds to the first challenge of supporting highly parallel and widely distributed data processing. In this setting, objects and relationships could prove to be a valuable abstraction to bridge the gap between the two partitions.
Introducing the concept of objects in cloud data management systems also makes sense from the perspective of addressing the second challenge of integrating different data processing paradigms. One advantage of MapReduce is that it can cast base data into different implicit models. The associated disadvantage is that the data model is constructed on the fly and, thus, type checking is only possible to a limited extent. To support typing of MapReduce queries, the same base data instances could be exposed using different object wrappers. Microsoft has recently proposed “Orleans”, a next-generation programming model for cloud computing that features a higher level of abstraction than MapReduce. In order to integrate different processing paradigms, Orleans introduces the notion of “grains” that serve as a unit of computation and data storage.
Finally, object database technologies can also contribute to addressing the third challenge, i.e. providing service-based interfaces for cloud data management. Since object data models and service-oriented interfaces are closely related, it makes a lot of sense to consider object database technology, rather than introducing additional mapping layers. The concept of orthogonal persistence, that is an essential feature of most recent object databases, is particularly relevant in this context. In their ICOODB 2009 paper, Dearle et al. have suggested that orthogonal persistence could be extended in order to simplify the development of cloud applications. Instead of only abstracting from the storage hierarchy, this extended orthogonal persistence would also abstract from replication and physical location, giving transparent access to distributed objects. Even though Orleans is built on top of the Windows Azure Platform that provides a relational database (SQL Azure), the vision of grains is to support transparent replication, consistency and persistence.
Q6. Do you know of any application domains where object database technologies are already used in the Cloud?
MG: From the major object database vendors, I am only aware of Objectivity that has a version of their product that is ready to be deployed on cloud infrastructures such as Amazon EC2 and GoGrid. However, I have not yet seen any concrete case study showing how their clients are using this product. This being said, it might be interesting to point out, that many of the applications that are currently deployed using object databases are very close to the envisioned use case of cloud data management. For example, Objectivity has been applied in Space Situational Awareness Foundational Enterprise (SSAFE) system and in several data-intensive science applications, for example at the Stanford Linear Accelerator Center (SLAC). Similarly, the European Space Agency (ESA) has chosen Versant to gather and analyze the data transmitted by the Herschel telescope. All of these applications deal with large or even huge amounts of data and require analytical data processing in the sense that was described before.
Q7. What issues would you recommend as a researcher to tackle to go beyond the current state of the art in cloud computing data management?
MG: There is ample opportunity to tackle interesting and important issues along the lines of all three challenges mentioned before. However, if we abstract even more, there are two general research areas that will need to be tackled in order to deliver the vision of cloud data management.
The first area addresses research questions “under the hood”, for example: How can existing parallel and distributed databases scale up to the level of cloud computing? What traditional database functionality is required in the context of cloud data management and how can it be supported? How can traditional databases be combined with other data processing paradigms such as MapReduce or data stream processing? What architectures will lead to fast and scalable data processing systems? The second important area is how cloud data services are provided to clients and, thus, the following research questions are situated “on the hood”: What interfaces should be offered by cloud data services? Do we still need declarative query languages or is a procedural interface the way to go? Is there even a need for entirely new programming models? Can cloud computing be made independent of or orthogonal to the development of the application business logic? How are cloud data management applications tested, deployed and debugged? Are existing database benchmarks sufficient to evaluate cloud data services or do we need new ones?
Of course, these lists of research questions are not exhaustive and merely highlight some of the challenges. Nevertheless, I believe that in answering these questions, one should always keep an eye on recent and also not-so-recent contributions from object databases. As outlined above, many developments in cloud data services have introduced some kind of object notion and, therefore, contributions from object databases can serve two purposes. On the hand, technologies such as orthogonal persistence can serve as valuable starting points and inspiration for novel developments. On the other hand, we should also learn from previous approaches in order not to reinvent the wheel and not to repeat some of the mistakes that were made before.
Acknowledgement
Michael Grossniklaus would like to thank Moira C. Norrie, David Maier, Bill Howe and Alan Dearle for interesting discussions on this topic and the valuable exchange of ideas.
Michael Grossniklaus
Michael received his doctorate in computer science from ETH Zurich in 2007. His PhD thesis examined how object data models can be extended with versioning to support context-aware data management. In addition to conducting research, Michael has been involved in several courses as a lecturer. Together with Moira C. Norrie, he developed a course on object databases for advanced students which he taught for several years. Currently, Michael is a senior researcher at the Politecnico di Milano, where he both contributes to the “Search Computing” project and works on reasoning over data streams. He has recently been awarded a grant by the Swiss National Science Foundation (SNF) for a fellowship as an advanced researcher in David Maier’s group at Portland State University, where he will be investigating the use of object database technology for cloud data management.
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New Resources
I published some new resources.
1. A new interesting User Report: 33/10 by Tilmann Zäschke. Tilmann used to work for the European Space Agency- His task there was to implement a persistence backend for the Herschel Space Observatory. The Herschel Space Observatory is a satellite that performs observations in the far infrared spectrum, in particular observing very old objects with a high red-shift. The life time of the satellite is limited to 3-4 years, during which it is expected to produce 15TB of data. You can download the User Report: 33/10.
2. An article by German Viscuso: “Using Object Database db4o as Storage Provider in Voldemort.” Voldemort’s local persistence component allows for different storage engines to be plugged in. In his article German shows how to create a new storage engine that uses db4o as storage engine in Voldemort. You can download the paper (PDF).
The source code is available under the Apache 2.0 license.
3. Three revised TechView Product Reports:
– db4o TechView Product Report -Updated July 2010
– Objectivity/DB TechView Product Report-Updated June 2010.
– ObjectStore TechView Product Report -Updated August 2010.
The jury has selected three finalists for the ODBMS.ORG “Best Object Databases Lecture Notes” Award 2010.
The three finalists are:
“Object Database Tutorial”
by Rick Cattell, Independent Consultant, USA.
“Object-Oriented Databases”
by Michael Grossniklaus and Moira Norrie, ETH Zürich, Switzerland.
“Modern Database Techniques”
by Martin Hulin, Hochschule Ravensburg-Weingarten, Germany.
You can download the three Lecture Notes here.
The Awards recognize the most complete and up to date lecture notes on Object Databases, that have been, or have strong potential to be, instrumental to the teaching of theory and practice in the field of objects and databases. Any Lecture Notes published in ODBMS.ORG during the years 2004-2010 were eligible for the 2010 award.
The jury panel was composed by:
Prof. Suad Alagic, University of Southern Maine, USA
Prof. Dr. Alfonso F. Cárdenas, UCLA, USA
Leon Guzenda, Objectivity, USA
John McHugh, Progress Software, USA
Prof. Renzo Orsini, University of Venice, Italy
Prof. Tore J.M. Risch, University of Uppsala, Sweden
Prof. Nicolas Spyratos, University of Paris South, France
Prof. Roberto V. Zicari, Goethe University Frankfurt, Germany.
The Award Ceremony will be on September 29, 2010, at the 3rd International Conference on Objects and Databases (ICOODB 2010) in Frankfurt.
Universal antipatterns.
I published two chapters of the new book of one of our distinguished experts: Dr. Michael Blaha.
Michael is one of the early pioneers in database modeling, together with his colleagues William J. Premerlani and James E. Rumbaugh. His classic textbook “Object-Oriented Modeling and Design” has been translated into many languages.
The new book of Dr. Blah is about Patterns of Data Modelling.
One of the chapter I published describes Universal antipatterns.
An antipattern is a characterization of a common software flaw. The idea is that as you construct models, you should be alert for antipatterns and correct them. When you find an antipattern, you should substitute the correction. Universal antipatterns are antipatterns that you should avoid for all applications. The chapter is available for free download (.pdf) in the Expert Section.
The other chapter is about Models.
Models provide the means for building quality software in a predictable manner. Models let developers think deeply about software and cope with large size and complexity. Although models are beneficial, they can be difficult to construct. That is where patterns come in. Also this chapter is available for free download (.pdf).
If you are in Frankfurt late September for ICOODB Frankfurt you can consider attending Dr. Blah`s tutorial: “”Patterns of Data Modeling”” on September 28, 2010.
RVZ
the OO7J benchmark.
I just published a very interesting resource in ODBMS.ORG, the dissertation of Pieter van Zyl, from the University of Pretoria.
The title of the dissertation is: “Performance investigation into selected object persistence stores” and presents the OO7J benchmark.
OO7J is a Java version of the original OO7 benchmark (written in C++) from Mike Carey, David DeWitt and Jeff Naughton = Univ
Wisconsin-Madison. The original benchmark tested ODBMS performance.
OO7J also includes benchmarking ORM Tools. Currently there are implementations for Hibernate on PostgreSQL and MySQL, db4o and Versant.
You can download the dissertation (187 pages PDF) : LINK
The code is available on Sourceforge: LINK
RVZ
