The Search for a New Stack

The Google App Engine Standard Environment and the ecosystem around it has provided a comfortable and solid software stack in the past.

There is a general move towards standalone services, such is the case with Tasks and Scheduler.

  • New ones were introduced (FirestoreMemorystore),
  • some of them were deprecated (mail, Channel API),
  • while the fate of a few are unknown (memcache, search).

There were a mix of changes on the (Standard) platform:

  • Version 2 runtimes, auto-scaling containers (gVisor)
  • Move to REST API-s, away from the proprietary Google API.

These are generally welcoming changes, and while the platform is in a state of flux currently, some of these changes have removed the comfort and ease of building software for the platform.

The search begins

Writing infrastructure code has eaten into IT projects for a long time. It causes all sorts of project slippages, budget overruns, and enormous technical debt.

Developing your own server – web, messaging, socket, even database – or building a new web application framework, or client library are all “writing infrastructure code”.

Therefore it is imperative that coding focuses on business capabilities and value with the new stack.

It also means we can do away with the server infrastructure – servers, VMs, containers. While we are at it, it should remove the associated scalability and availability concerns too.

Costs should align with business value, and include effective use only, for example: computing time (no idle), messages sent, users registered, data stored, files transferred, jobs scheduled, and so on.

The stack has to sit on a platform with a rich ecosystem – a good range of core services for example:

  • database,
  • object storage,
  • messaging,
  • identity management.

Some of the best in class capabilities could live outside of the platform, for example:

  • payments,
  • bulk and transactional emails.

The stack must have the facilities to gain insight into its inner workings. Support for variable levels of centralised logging from hosted services and from logging API in bespoke code.

Support for monitoring of platform and application services, alerting on specified issues and metrics.

There must adequate tooling support for the entire application lifecycle extended to

  • environments, including local development,
  • programming languages, including relevant tool-chains.

A worthy candidate

The emerging paradigm, Function as a Service (FaaS) together with Serverless, promises to meet my expectations. It would have to be part of a comprehensive cloud offering that delivers the rest of the stack.

FaaS/Serverless reinforces cloud native concepts, and that leaves a lot of questions open for this new stack.

There is still the Google App Engine Standard 2nd Generation with the rest of the services from the Google Cloud Platform. If it wasn’t for the challenges that prompted the search for a new stack, it would be one of the best options available. Perhaps it will be again some day. In the meantime, is there something better?

Is it going to deliver on the benefits? What are the trade-offs going to be? Let’s see.


Robo-Advisors: Digital Transformation in Investment Management

When hearing the expression robo-advisor, you might be thinking of Siri, or Alexa, or one of their robo-siblings giving you advice about the next big investment opportunity. That’s not what these robots are about, at least not yet. Rather than giving advice, at the moment robo-advisors are nothing more than automated portfolio managers. These digital investment services with minimal human intervention are aimed at investors who want inexpensive savings plans and a convenient way to manage the investment choices.

Digital Transformation in Financial Services

Meet Tim, head of product and one of the founders at a rapidly growing FinTech startup. He started by telling us about the early days of their company.

…algorithms have come a long way and together with new technologies they have transformed everyday activities in ways that allow cars to assist people with driving, make recommendations for music to listen to, news to read, even match people for a date. Recent disruptions to the financial services sector target how we make payments, arrange lending, and consume advisory services.

Digital transformation has been upon the financial services sector for some time. Tim’s company discovered that the lack of innovation in parts of the sector made it an ideal target for disruption.

We realized that a segment of advisory services was ripe for automation, that’s how we ended up in wealth management. The business processes for these services are simple enough to allow automation of them to a great degree — provided that the needs of our customers fall within a range with regard to acceptable risk, investment choices, and mode of interaction.

He continues:

Until recently, the two options for investing were self-invest (DIY) or the involvement of asset managers for fee. Recent studies about customer satisfaction also revealed that financial services ranks at the bottom of the industry sector list. Many customers had bad experiences with advisors, which prompted the question: would they trust a machine more than a human? We saw an opportunity to offer a third option: automated investment services. The technology was already available to us to build our digital product – smart algorithms backed by deep learning, interconnected digital ecosystems, cloud hosted platforms.

Tim explains how they differentiated their product in this space:

When we set out to define our product and services we specifically focused on making them to…

Be easily accessible – starting with web access, and increasingly putting more and more effort into the mobile channel.

Be more economical by reducing costs significantly, and making them accessible to a larger customer base.

Be more transparent about pricing, risks, and really all aspects of the services we offer.

The time was also right for tapping into a new breed of customers, who are increasingly digitally literate and open to exploring new channels and ways of interacting not only with their peers but also with businesses.

Financial services is a prime target for agile, digital companies eager to transform the customer experience and automate operations. Tim’s company had to move fast:

We wanted to launch quickly because of the fierce competition between providers of financial services fueled by the latest technological innovations. We have launched our robo-advisor product to a group of early (beta) customers in 6 months.

What is the market for robo-advisors?

Ana is a twenty-something graduate from a top school, currently working at one of the multi-national firms. She is a typical millennial in many ways – enjoys hanging out on social media, and updates friends using a variety of messaging apps. Time is important for her, she hates queuing in line, gets food delivered to her, and works strictly office hours.

Although she has a lot of time for friends, followers, and listens to influencers, she first heard about investing from her senior colleagues during a conversation. It was not long before she came across Tim’s company offering investment products to young folks like her.

She commented:

The ease of use was a big plus. It completely demystified complex financial products and services.

I liked that I could invest small amounts of money. I can only afford to save a few hundred per month at this time.

I also liked the personalized services made available right from the app, and the fact that I did not have to go to some branch, queue, deal with paperwork, and engage in boring small talk just to open an account.

Customers of recent generations, in general, are more comfortable with interacting with digital services than previous ones. The digital customer journey is, and will be, a differentiator amongst robo-advisor providers. Not only for making customer acquisition more efficient, but also for retaining customers for the long term. This tendency allows services in general to replace humans with automated tasks, and direct interactions to purely digital channels such as web, chat-bots, apps, and others.

Tim’s reflection on the market:

Millennials are increasingly looking for ways to manage their money, with cheap investment options, and economies of scale, our robo-advisor services are able to fill a gap for this customer segment.

There is also increasing evidence suggesting that actively managed funds, and their human managers, are unable to produce the returns that plain index funds can deliver in the long term. This gives us the opportunity to look at new customer segments and also engage with Baby Boomers at or approaching retirement, and High Net Worth Individuals for our new services.

Investment advice is inherently complicated for most people — anything associated with financials tends to be complicated naturally. Robo-advisors allow a set it and forget it approach to investment, putting it on auto-pilot that performs best in the long term.

Tim also admits, it has not been a smooth ride for the company.

While the technology is in place, and the market together with demand is seemingly there, the marketing efforts and costs involved in turning our robo-advisor into the product we wanted to offer was a grueling task. It is a service that competes on ease of use, and most importantly on price.

The rock-bottom pricing means that we must seek to profit from the economies of scale, and long-term customer loyalty. Achieving a higher customer lifetime value over customer acquisition cost in this environment is not a trivial task, and puts our marketing and sales departments on their toes.

At the same time, technological advances have created an environment that makes product innovation, development, and launch increasingly rapid. Digitization of capabilities and processes have allowed the introduction of new products and business models, and lowered the barrier for entry for many new businesses. The marketing challenges and increasing competition put Tim’s company under pressure.

As soon as the first version of our product was out, we started working on the next iteration. We had major plans, and an ambitious roadmap.

A Day in the Life of a robo-advisor

Two years after those first beta users at Tim’s company, multiple robo-advisor platforms sprung up sharing the same mode of operation:

  1. Customer acquisition
  2. Customer assessment, investment portfolio selection or construction
  3. Continuous portfolio monitoring and rebalancing

Tim explains:

Customer acquisition is a critical step in the process although not tightly related to the advisory activities. As with many digital offerings, the marketing and selling of the service is just as important as providing a good service itself.

It is the customer assessment, investment portfolio construction, and rebalancing activities where our smart algorithms take center stage and differentiate us from others. The assessment is based on a customer questionnaire with the purpose of homing in on the critical parameters of the most suitable investment portfolio.

Modern Portfolio Theory provides the foundation for portfolio construction, informed by the goals and parameters — such as appetite for risk or expected returns, investment duration, amount of money invested, and so on.

For most services, the universe (a set of securities with common features) for selecting investment from is limited to ETFs. For customers with larger investable amount on their account, we also offer the option to allocate a portion of the investment to trading individual shares from the stock market. Our system regularly rebalances the portfolio, based on our proprietary strategy, to maximize the performance considering current market trends and industry performances.

Naturally, we also send regular updates to our customers about the performance of their investment, and inform them about our efforts to better manage their assets.

Although nobody should expect to become a financial guru from using robo-advisors, user experience plays a critical role in eliciting a more thoughtful customer behavior for managing finances. People tend to overestimate their tolerance for risk when it comes to their investments. Robo-advisors allow for more guided and informed decisions on investments, and protection of assets from unwanted risk.

Transforming an industry is not an easy task

Let us not forget that many of the industries in existence today, including the once from the financial services sector, have been around for a long time. In comparison, the Digital Transformation movement is in its infancy. Bringing together people, process, and technology to make business successful remains a challenging task – a statements Tim agrees with.

While most robo-advisor services, including ours, are just front ends to existing financial systems, the effort going into providing an excellent user experience to our customers on desktop and increasingly on mobile channels cannot go unnoticed. Therefore, removing the worry from the process, offering a hugely valuable service, while hiding the complexity of the underlying financial systems is a feat that few of the robo-advisors are capable managing well.

The opportunity for robo-advisors comes from offering cheaper, superior investment advice, and reaching a massive market with the low-cost service. These services charge roughly an order of magnitude lower rates (0.1% instead of 1%) to manage individual portfolios. At these rates, client acquisition remains a question and a challenge for most robo-advisors.

We have asked Tim – how do you make sure the profitability of the customer grows beyond the costs of acquisition?

“Indeed, some of the services fell for the if you build, they will come fallacy, and ignored the significant effort required on the digital marketing side.

After recognizing the marketing challenges, we immediately started looking for new opportunities to turn profit. We set out two paths in our strategy:

  • Partnering with established financial firms with wealth management arms, which could allow us to tap into a more affluent segment of the market.
  • Discovering new channels and digital marketing platforms for reaching customers at a lower cost, experimenting with multiple social media channels.”

What is the reaction from the financial services industry?

Most recently, robo-advisor companies looked to move from direct to consumer (B2C) to partnering with existing advisors and adopting a business-to-business (B2B), or B2B2C model. The immediate opportunity was in leveraging automated advisory services and augmenting human advisors.

Rob who works at one of the largest bank’s wealth management arm explains:

The emergence of ETFs and index trackers gave rise to a simpler and lot more economic investment option: passively managed investments, as opposed to actively managed funds and portfolios involving human fund managers.

At the same time, robo-advisors have transformed the way investments portfolios are constructed and managed. Robo-advisors have the additional benefit of simplifying, even removing the complexities of tax implications, investment methodology, and philosophy choices.

We have observed that some people have difficulties entrusting a computer to manage their investment wholly, something that lacks personality, empathy, and human intuition.

We believe that the emerging trend of hybrid robo-advisors provides a solution by mixing automated and traditional human services. With the hybrid model, human advisors can focus on what they do best — consulting the client about their financial position, and needs on a more personal level — and scale their services at the same time.

Until the underlying products and technologies for automated investment advisor services develop further, human advisors will have the benefit of leveraging current automation services, and augmenting their own offerings.

Rob’s bank has tremendous interest in robo-advisors and Rob is leading the effort internally. His team recently started working with Tim’s company.

We have looked at a variety of products and features available on the market at the time, and found a wide spectrum across the board.

The summary some of the findings from the bank’s research analysis focused on the limitations and differences among the automated investment advisory services:

  • The level of personalization provided by the robo-advisors varies widely, but in general remains limited. A degree of personalization comes from the variety of niches available in this space aimed at women, students, or Islamic investors for example.
  • Most services select pre-constructed portfolios based on a limited set of outcomes from the customer assessment.
  • Investment strategies and features such as tax optimization, tax harvesting, and rebalancing are not available to all services, or to all customer segments.
  • Tax-loss harvesting, for example, requires buying individual shares rather than ETFs in order to account for losses in individual shares.
  • User experience, from onboarding to regular updates, is a huge differentiator among the businesses.
  • In a similar fashion, channel strategy and digital marketing also set many of these services apart.
  • There is also a geographic differentiation of these services, driven by regional specifics such as state of economy, regulation, and customer behavior.
  • The countries with the most robo-advisor services at the time were: United States, United Kingdom, Canada, and Australia. Asian and South American countries were also represented in much smaller numbers.

Rob summarized the bank’s stance on the industry itself:

We have been pleasantly surprised and happy with the hybrid robo-advisor model. The partnership with Tim’s company allowed us to introduce a new set of products for our clients.

Automated investment advisory services made an impact on the wealth management industry and will continue to grow. We believe it will take a long time for robo-advisors to claim a significant portion of all assets under management. Until then, human advisors will continue to have a significant role in this industry.

What is the future for robo-advisor services?

We also asked Tim what he thought about the future directions for the industry.

Ultimately Robo-advisory services would be able to learn about the client’s’ financial needs and investment behavior to advise based on up to date individual characteristics.

Data collection and analysis will always have a tremendous role in collecting, connecting of relevant data, and in turning it into meaningful information for machine processing. Forming the ideal data set for the best advisory service will have to overcome privacy and security concerns, as well as quality issues for an ever-increasing mountain of data.

Future development stages of strong machine intelligence will be able to bring new levels of personalization to investment advisory. They will gradually incorporate qualitative (as opposed to quantitative) assessments in portfolio allocation, and will eventually develop an edge over their human counterparts. The research in artificial intelligence will fuel new developments in these fields, and increase the benefits to the clients of investment and other advisory businesses.

Final thoughts

ETF-s brought the democratization of financial services by their simplicity and low-cost structure. Social media platforms will further democratize the access and delivery of them, and the services based on such products. The combination of market trends and new technologies gave way to the emergence of robo-advisors. Advances in the fields of data analytics, and research in artificial intelligence will gradually remove current limitations, and services will eventually evolve beyond today’s best human advisors.

In the future, we can expect Alexa to wake us up to some good news about our financials.

Hello Dave, your portfolio is looking well today. Last night, I have reallocated some moderately performing investments into a higher risk/benefit asset in line with your preferences. Would you like to know more?


Managing Enterprise Architecture using Semantic Wiki

The basic idea is to use Semantic Web technologies to support the Enterprise Architecture practice in the organisation. Semantic Web offers a set of capabilities that makes it ideal for this purpose.

What is available?

  • Semantic MediaWiki
    The current version (at the time of writing: 1.8.x) is a sufficiently mature implementation of semantic features on top of MediaWiki. There are a suite of extensions – most of them are part of the Semantic Bundle – available to extend the basic feature set.
  • Enterprise Architecture ontology
    The basis for enabling SMW to be used as an Enterprise Architecture (EA) tool is the EA ontology (call it meta-model, or schema). It defines a set of concepts and their relationships that will help to organise and structure the information gathered about the enterprise. One of these ontologies can be easily derived from the ArchiMate (V2) meta-model.
  • Triple storage for semantic inferencing and querying (SPARQL)
    Triple stores are a bit tricky in the current version of SMW. The main reason for using them is to take advantage of features like: symmetric properties and inverse properties, offering a lot of value when querying.

What else is needed?

  • Semantic Annotation
    Currently the weakest part of MediaWiki (not specific to SMW) at the moment is the editing, which is not helped by the additional markups for semantic annotation. DataWiki (formerly SMW+) a reasonable job with allowing semantic annotation on the wiki page in WYSIWYG mode. A decent annotation tool (perhaps porting DataWiki’s implementation) is needed to do this job better than the plain wiki markup in SMW.
    Note that most people solve this issue and prefer to use Semantic Forms to enter data. It is a good solution, but carries the danger of  constraining information capture and limiting use (virtually turning SMW into SharePoint or an Access Database).
    This will require new development and/or porting of existing code.
  • Spreadsheet integration
    The reality is that most organisations still use and prefer Excel as the de-facto standard for gathering “somewhat” structured information. It is still the most effective way to request records of data (for example: application names, descriptions, owners, etc) and review of data in many organisations.
    This will require new development.
  • Visualisation tool
    Most people prefer visual representation of landscape, design, etc. and the preferred tool in most places is Visio.This will require new development.

Enterprise Architecture:The Common-Place-Book

The book I have just finished reading – Where Good Ideas Come From by Steven Johnson– had an interesting chapter on The Slow Hunch innovation pattern.

The part in there that really caught my attention was about the common-place-book. The following historical quote from John Mason in 1745 makes the case for organised (retrievable) thoughts:

Think is not enough to furnish this Store-house of the Mind good Thoughts, but lay them up there in Order, digested or ranged under proper Subjects or Classes. That whatever Subject you have Occasion to think or talk upon you may have recourse immediately to a good Thought, which you heretofore laid up there under that Subject. So that the very Mention of the Subject may bring the Thought to hand; by which means you will carry a regular Common Place-Book in your Memory.

In the same chapter, the historian Robert Darnton is quoted on re-organising texts into fragments and removing the linearity of the text:

Unlike modern readers, who follow the flow of narrative from beginning to end, early modern Englishmen read in fits and starts and jumped from book to book. They broke texts into fragments and assembled them into new patterns by transcribing them in different sections of their notebooks. Then they reread the copies and rearranged the patterns while adding more excerpts. Reading and writing were therefore inseparable activities. They belonged to a continuous effort to make sense of things, for the world was full of signs: you could read your way through it; and by keeping an account of your readings, you made a book of your own, one stamped with your personality.

Later in the Serendipity chapter – another pattern involving accidental connections – the author mentions DEVONthink, a tool to manage and organize all those disparate pieces of information. DEVONthink features a clever algorithm that detects a subtle semantic connections between distinct passages of text.


Systems – an Ontology approach

The purpose of the Systems Ontology is to provide a framework, in the form of an ontology, for capturing system details resulting from systems analysis.

The Systems Ontology is composed of three distinct parts:

  • SystemThing holds the core concepts of a system – not to be changed
  • SystemSpace is a definition of categories applicable to different concepts describing specific systems – should change rarely as the understanding of the different types of systems evolves
  • System is the place for the instances of specific systems – may change regularly as the analysis and understanding of specific systems progresses.

The following diagram is a representation of the different elements in each part.

Core concepts and Spaces
Core concepts define the fundamental building blocks for the Systems Ontology in a set of abstract classes of mainly two types: Thing and Space.
Things describe the different aspects of a system on very high abstraction level (meta-meta in this case).
Spaces provide further refinement for the different aspects of a system.

The structure of the system is described in abstract sub-classes of StructuredThing. The structure defines the relationships between concepts, it defines what form (in terms of graphs) of construct can be built. Constructs can be for example: chain, ring, tree, net, a combination of these or any other formation.

Specific structures are defined inheriting from the StructuredThing abstract class, making StructuredThing a collection, a container of various types.

An example structure: ComposableThing has two slots, has_part and is_part_of (both are each other’s inverse), since has_part is a multi-value, is_part_of a single-value slot referring to instances, the structure for ComposableThing is going to be a tree.

Categories are defined in the CategorySpace, which holds a hierarchy of categories underneath. CathegoryThing enables instances of a system elements to reference multiple categories from the CategorySpace.

CategorySpace is a bit unusual as far as modelling categories goes. Usually there is a meta-model for describing the characteristics of a category on the meta-model level then each category is an instance in the model often in some form of relationship with other categories.
In this case, a different, perhaps unusual approach is taken when categories are defined as classes and sub-classes of classes. This will allows to build a hierarchy of categories and use the class name for the name of the category, which should be sufficient as no attributes are necessary.
There is one more twist, instead of assigning a class (category) as a superclass to the categorized class, the CategorizedThing defines a slot with the value type of a class, in other words the category is defined as an attribute (slot).

This approach allows to define the categories in the meta-model in a hierarchy and still use them as an attribute in the model.

The notation of life-cycle makes the concepts “alive”, in other words, it represents the time factor.

Individual life-cycles are captured in the LifeCycleSpace as sub-classes. Life-cycles consist of states, these are captured as instances of a life-cycle class in the ontology. LiveThing enables the system instances to reference individual states and other related instances – behaviour and quality (details in the next section).

Note that life-cycles are not described as proper state-machines in this ontology. There is no notation of events or state transitions other than registering the preceding and following states for each state instance.

Behavior and Quality
The following assumption was made: Only “living” things (ie concepts with life-cycle) show characteristics of behavior and qualities.
Based on this assumption, behaviour and quality details can be registered to any LivingThing.

Behavior and Quality are two concepts represented in the ontology in a similar fashion.
BehaviorSpace consists of sub-classes, where sub-classes are considered as categories of behavior. These categories can serve as a mechanism to group, qualify, arrange the long list of different behaviors a system may have.
Specific behaviors are captured as instances of a Class from the BehaviorSpace.

Qualities are represented the same way, where categories are sub-classes of the QualitySpace and specific qualities are instances of a class from the QualitySpace.

System instances
The root for capturing specific system instances is SystemSpace, it inherits a set of abstract classes (LivingThing and CategorizedThing) describing a system.

Besides creating instances for system elements a few other types of instances will be created along the way including: life-cycle states, behaviors and qualities.

Life-cycles consist of states, individual state instances should be created under the relevant life-cycle categories.
States are later referenced from system instances, where a system can only have one state at a time in the current setup of the ontology.

Behaviours and Qualities
Behaviours and Qualities are instances of simple classes in the pertinent categories. In the current ontology each instance is represented with a name (slot) only.
The categories should be carefully chosen for both set of concepts, otherwise can be time consuming to re-factor an instance of a quality into a category in order to register finer grain qualities. Same challenge applies to registering behaviour.

Topology and Systems

Systems, and sub-systems, are captured in some form of hierarchy in the ontology – the Topology. The Topology for systems is not pre-defined as it mostly depends on the method applied to systems analysis, and somewhat depends on the different types of systems as well.
Topology is withing the System ontology, it is captured together with the system instances, unlike the categories for life-cycle, behaviour and quality.

System instances are created within the topology, where they automatically inherit characteristics of LivingThing and CategorizedThing.
The topology does not have a notation of structure for system elements, therefore predefined (in the SystemSpace) structures should be applied as Super-classes to specific topology classes. Note that sub-classes in the topology will inherit structures from parent classes, for that reason structures should be used sparingly in classes closer to the root and should be applied to classes closer to the instances.

Data characteristics of system elements are defined within of the Topology by adding them to specific classes. Data details are essentially attributes (slots here) on a class. One should be careful making sure that data is not confused with structure. Slots with references to other system instances are perfectly valid data elements, however these can be easily confused with structure elements also represented as slots. The success of proper separation will depend on the rigour applied to systems analysis.


Code Generation 2008: Day 3

Last day of CG2008.

Key note: The Domain Specific IDE by Steve Cook (Microsoft)
Steve has introduced DSLs briefly, talked about various patterns of use, then the main topic of software development using DSLs and the evolution of software development. He depicted SW evolution in quadrants and highlighted the path of evolution as: craftmanship > mass production > continuous improvement > mass customization.
A few highlights from the session

It makes sense to model concerns [IT Architecture].

[DSL, code generation] the only way to get some head-way is to make it economical.

To create a language [DSL], it really means to create a tool. The language itself is pretty useless in itself.

We have recognized that Visual Studio is a platform not just an IDE.

Bran Selic: Standardization [software development] goes way beyond standardization of API. One way to do it right is to standardize on semantics.

Experience Report: Evangelizing Code Generation: A case study of incremental adoption by Brooke Hamilton (FM Global)
Brooke’s session was pleasantly different than the other sessions during the conference as he was from the customer side and not a vendor or an academic. His presentation was brilliant, well structured, pleasantly constructed. Brooke had real details of his own and his team’s experience on their project(s) from the last few years. It was definitely one of the best presentations during the conference, it sparked a lot of good discussions.

Code Generation Narcosis – you feel more powerful using code generation than you really are.

Code generation improves architecture. It makes you look for redundant parts and separate concerns.

We cannot make an application serving unknown needs, but we can generate a lot of code! 

Versioning of the templates [code generation] is absolutely crucial.

Tutorial: Strategies for generating code from Microsoft DSL tools and T4 text templates by Brooke Hamilton (FM Global)
This session was more of a demo using the Microsoft DSL Tools in the Visual Studio environment. Brooke also tailored in some of his experience with the tool and how it is applicable to their projects at FM Global.

* * *

In summary, it was an excellent conference with a lot of great presentations from the top experts on this field, great discussions during panels, BoFs and Goldfish Bowls. A lot of new ideas, thoughts and questions to take away to consolidate and look for answers until next year’s event.


Code Generation 2008: Day 2

Second day at CG2008.

Tutorial: Model-Driven SOA: Synchronize Business Planning with the IT Design process by Ian Barnard (Telelogic)
Telelogic, recently acquired by IBM, presented a very corporate like view of modeling, the key buzzwords of the session were: EA, BPM, SOA, ELM (Enterprise Lifecycle Management), Model-Driven SOA…

Tutorial: Implementation Techniques for Domain-Specific Languages by Markus Voelter (Independent)
Markus is an entertaining presenter, he has a broad knowledge of the various topics around code generation and DSL. His customer experience also comes across and he makes genuine remarks about IT architectures and business-IT alignment.
This particular presentation was a deep-dive on DSL and implementation, categories of DSLs and their use, DSL implementation techniques and examples including: ScalaRubyConvergeEMF/GMFMetaEdit+

DSL does not have to be Turing complete!

execution != precision

Experience Report: Can executable UML make it as a Mainstream Programming Paradigm? by Allan Kennedy (Kennedy Carter)
The presentation started with a fair amount of philosophical discussion, involving the audience, about software intensive systems, software engineering, and modeling. Later Allan introduced the concepts, the use and value of Executable UML (xUML).
xUML seems to have impressive customer base and references including some defense industry customers and NASA.

Tutorial: Best practices for Creating Domain-Specific Modeling Languages by Juha-Pekka Tolvanen (MetaCase)
JP has recently published a book on Domain Specific Modeling (DSM) and I was very much looking forward to hear the details from the author himself. JP gave plenty of examples and details on best practices, lessons learned from his extensive customer experience. A great value from the presentation was to hear concrete numbers for developing with DSL. The statistics included variations for language complexity (number of concepts) and the complexity of artefacts generated (number of them and types).

Panel: Flexibility in Code Generation chaired by Jos Warmer (Ordina), Sven Efftinge (itemis), Anneke Kleppe (CapGemini NL), Laurence Tratt (Uni of Bournemouth), Steven Kelly (MetaCase)
The panel has eluded into many different topics in many directions including flexibility in DSL and code generation. From the questions and comments it seems that these concepts still have a long way to go to be more mature, precise and agreed on.

Unfortunately I’ve missed the Mistery Movie of the evening – Pirates of Silicon Valley – will catch up on that one day…