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About Project Zero and WebSphere sMash

Project Zero began life as an incubator project to explore a new idea that we believed had promise. That idea was of a development and runtime environment that could revolutionize creation of dynamic web applications -- providing a powerful development and execution platform for modern Web applications while at the same time having the overall experience of being radically simple. We started this incubator because we wanted to address the complexities of modern Web applications without the chains of previous architectures, technologies, or decisions.




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We're using ProjectZero.org to incubate the technology, offering an open window into the development process, the code and the developers. You can be as involved as you like, from 'just looking' to 'fully engaged'. From anonymous download to registered users, trying the code, reviewing the source, giving feedback, sharing experiences, making recommendations and providing requirements. We've delivered two product versions of IBM WebSphere sMash based on the work here and are embarking on our third experimental code stream, codenamed Sebring.

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WebSphere sMash is a development and execution environment for dynamic web applications.

WebSphere sMash advances Smart SOA's simplicity and accelerates the alignment of Business and IT by allowing Developers to quickly deliver dynamic Web 2.0 based applications.

* Based on the highly-acclaimed public incubator, Project Zero. Learn more and download free of charge.
* Supports some of today's hottest dynamic scripting languages - PHP and Groovy.
* Unleash and reuse enterprise content, including SOA services, as RESTful services
* Enable rapid aggregation of disparate services and feeds
* Extends the reach of SOA – effectively using the web as your SOA platform
* An agile development environment - small footprint, easy to modify, fast re-start
* Partners can easily deliver solutions for mid-market and departmental level needs

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InfoSphere™ MashupHub, the cornerstone of the Info 2.0 vision, is a lightweight information management environment for IT and business professionals who wish to unlock and share web, departmental, personal, and enterprise information for use in Web 2.0 applications and mashups. MashupHub includes visual tools for creating, storing, transforming, and remixing feeds to be utilized in mashup and situational applications, and a central catalog for users to tag, rate, and share mashable assets.

InfoSphere MashupHub Key Value Propositions

* Ability to “Web 2.0” enable existing systems without requiring changes to the underlying system or duplication of the information source.
* Advanced situational transformation & mixing of Enterprise, Web, Personal and Departmental information sources for Web 2.0 applications and mashups.
* Connectors for Core Enterprise Systems and common information sources.
* Enterprise-class Security and Governance.
* Scalability, Reliability, Performance & IBM Support.

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IBM® Lotus® Mashups provides a lightweight mashup environment for assembling personal, enterprise and Web content into simple, flexible, and dynamic applications. With Lotus Mashups, Web-savvy business users can easily create and share new applications that address their immediate business needs. Lotus Mashups is currently available as part of IBM Mashup Center — a complete solution which helps organizations improve productivity and effectiveness.

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IBM mashup RIA Lotus web enterprise dashboard

IBM® Mashup Center is designed to provide an easy to use business mashup solution, supporting line of business assembly of dynamic situational applications - with the security and governance capabilities IT requires.

With this lightweight mashup environment, organizations can unlock and transform enterprise, Web, personal and departmental information into consumable or "mashable" assets, including information feeds and widgets. These assets can then be dynamically assembled, at-the-glass, into new applications that address daily business challenges. With IBM Mashup Center, organizations can reduce their application backlog and improve productivity by empowering line of business, self-service application development.

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Dec. 18 (Bloomberg) -- International Business Machines Corp., Sun Microsystems Inc. and SAP AG may be among the hardest hit technology companies in 2009 as spending on computers, software and services drops for the first time in six years.

Spending will decline 4 percent, led by an 8 percent slump in developed markets, Goldman Sachs Group Inc. estimates. The first quarter may be one of the worst ever for software makers, which may cut 5 percent of workers or more in 2009, said Brent Thill, an analyst at Citigroup Inc. in San Francisco.

A worsening economy and the bankruptcies of Wall Street icons such as Lehman Brothers Holdings Inc. have prompted companies in all industries to curb purchases and preserve cash. While demand has slowed, budget cuts haven’t yet had their full impact on technology orders, Gartner Inc. analyst Ken McGee said.

“There’s going to be a period of reckoning that’s not going to be pretty,” he said. “There is an unhealthy incongruence between the state of economy and the number of companies that are saying their information technology staffs are extremely busy.”

The U.S. economy, in a recession for a year, may suffer its longest slump in the post-World War II era as job losses mount. The economic decline will cause companies to cut technology budgets in the U.S., Western Europe, Canada and Japan, Goldman Sachs said in a Dec. 1 report. In emerging markets, such as China and India, growth in technology spending is slowing.

Projects Canceled

“I’m not sure if I am going to have any money to do any new projects, and we will be reassessing our existing projects,” said Chris Vein, chief information officer for the city of San Francisco, which is contending with a $576 million budget shortfall.

Half of CIOs are looking to cut consulting-services costs, 35 percent want to reduce computer and server expenses, and 23 percent want savings on software, according to a Goldman Sachs survey.

Financial companies, accounting for the biggest single chunk of technology spending, will cut budgets as much

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Pandora, Last.fm, and iLike have broadened the appeal of recommendation algorithms and social discovery and sharing. Despite a challenging – even hostile - business environment, passionate entrepreneurs and music lovers are bringing to market new ways to explore and discover new music.

Mufin has gathered the most attention so far – being a spin off of the Fraunhofer Institut. Mufin relies exclusively on content analysis to make music recommendations. The service analyzes 40 characteristics for each the 4 million songs in its database, including tempo, sound density, and variety of other factors to filter out songs which are similar. This content-based approach uniquely addresses cold start and long tail discovery issues, but feels a bit too “literal” in the way it analyzes music similarities between songs.

The perceptron is an experimental and transparent collaborative filtering service. The service aggregates and filters recommendations made by actual humans (from tinymixtapes.com and epitonic.com), social links between artists and fans (e.g. belonging to the same label or the number of friends on a myspace page), and other social music sharing sources. Interestingly, recommendations sources and the relative weights of the various sources are all public. The service does a great job at mixing familiar and unknown recommendations, thereby making the new stuff all the more interesting. It would be terrific to see a next generation of this service allow users to actually tinker with sources and weights to obtain more personalized recommendations.

The next big sound pushes the music discovery envelope. The service does a great job at crowd-sourcing and uncovering new musical talents, using the music label paradigm. Each listener is allowed to select and sign up to 10 artists at any given time among all the unsigned artists that broadcast their music on the site. This site is a great place for unadulterated creative talent, although a bit of collaborative filtering would enhance the listening experience.

With Bla

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Gabe Rivera of Techmeme fame has sparked a lively debate as he announced he had hired an editor to improve interestingness, reactivity and relevance of his news aggregation website (see VentureBeat, TechCrunch, and ReadWriteWeb). Rivera’s plight echoes Netflix’s challenges at boosting its movie recommendation engine, as algorithmic improvements gradually near their natural asymptote.

Techmeme is good at aggregating news overtime but not at breaking them, pointing to several common datamining and recommendation challenges:

- “Cold start” – interestingly digg’s social voting approach hasn’t been able to overcome that challenge either – in both cases, the services cannot anticipate news’ propagation and velocity;

- Context – Techmeme sometimes mixes up headlines and for instance ended up featuring news about Anna Nicole Smith’s hospitalization after she’s already been declared dead;

- Outliers or the “Napoleon Dynamite” problem, as the New York Times dubs it - identifying newsworthy pieces from uncommon sources before they make it into the mainstream is also an issue.

Interestingness and relevance are Techmeme’s other key reasons to bring in a human eye. Techmeme bets that an expert hand can be a better judge than crowdsourced implicit feedback based on clickstream or explicit feedback such as social voting. This approach seems to contradict much of the crowdsourcing mantra, although Techmeme’s case is more about rebalancing than shunning crowdsourcing.

For online retailers, Techmeme’s move to “curated news aggregation” highlights opportunities to blend human input, datamining, and recommendation:

- to add context to a product recommendation – based on usage, audience background, as well as internal needs through promotions;

- to identify new and unexplored relationships between products – for product discovery, up-sell, and outliers.

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For the second year, Netflix has awarded a $50K progress prize. The winning team – BellKor in BigChaos – improved on Netflix’s recommendation algorithm by 9.44%. Although the 10% improvement seems to be getting closer in absolute terms, the last stretch might prove elusive for a little while longer.

Is Netflix’s ground-breaking crowdsourcing approach reaching a limit? Netflix has undeniably got a lot of value out of their contest – 10,000s people working and an algorithmic improvement very close to 10% – for only $100K so far. However, the winning method this year – and the likely research directions for next year – have exponential data and computing power requirements – possibly putting the contest out of reach of most casual researchers.

Are “curated” recommendations superior to pure statistics? Curated recommendations have received some hype lately – from Techmeme’s hiring of an editor to the coincidental launch this week of ClerkDogs – a “clerks-in-a-box” online movie recommendation service? A human touch can certainly rebalance or add a forward-looking perspective to recommendations where data sets are ambiguous (such as clickstream), too small, or lack enough historical data.

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Apple has quietly released a few changes to its iPhone App Store on iTunes, in an attempt to alleviate some of the growing pains around its app ecosystem. The App Store has recently hit 10,000 apps and is expected to accept many more.

“Most popular” lists and 19 high-level categories are hardly up to the task of helping consumers find new apps. Limited discovery is hampering the App Store’s growth and monetization and could have a depressing effect on the ecosystem.

There are however quite a few opportunities for Apple to improve discovery and bring the overall shopping experience on the App Store away from simply being a “port” of the iTunes Musicstore:

- Improve discovery through experience

- Enable free trials – free trials are critical to bring feature-rich and higher priced apps to the ecosystem.

- Add videos and user video reviews – text and screenshots do little justice to an app’s capabilities, design, and overall user experience;

- Improve discovery through richer feedback and needs analysis

- Categories and search are limited tools for demand generation;

- Amazon-style recommendations and discovery needs to be more ubiquitous and prominent on the App Store to enable genuine comparison between apps;

- Blending the feedback process about apps much with the user experience on the iPhone could open up a much more personalized experience

- Improve discovery through finer segmentation and filtering – the free vs. paid segmentation is dragging all paid iPhone apps down in the ringtones pricing range; there are only 14 apps over $100 (out of 10,000) and there are still relatively few games at a price comparable to that of a typical game console.

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