MétaCan
Menu
Back to cohort
Record W224468805

SOA (Begins to Make) Inroads: The New/old Approach to Technology Design Edges into "Early Majority" Use

2006· article· en· W224468805 on OpenAlexaboutno aff
Lauren Bielski

Bibliographic record

VenueABA banking journal · 2006
Typearticle
Languageen
FieldComputer Science
TopicService-Oriented Architecture and Web Services
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetService (business)Product (mathematics)Financial servicesOrder (exchange)BusinessWorkflowMarketingEngineeringManagementComputer scienceEconomicsFinanceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

For the skeptics there, word has it that service-oriented is real and it's really being done--however incrementally. Consultants say many of the largest U.S. banks are a fast follow to the activities of European and Asian leaders. One the largest Dutch banks, for instance, has adopted SOA broadly to initiate a major redesign of all lending and customer service processes. transition has led to more efficiency and a better customer experience. Senior research analyst Jerry Silva, with TowerGroup, Needham, Mass., says that the SOA projects he's helped on here are mostly tactical and even modest. But this isn't bad news, says Silva. In fact, most of the banks are thinking very strategically about services, and seeing how they could get to an enterprise play. Silva explains that there is a think global, act local mindset, in order to work the kinks with the technology and get some immediate investment return. He and others offer these elusive tidbits: One global giant based here is counting on SOA to retool its complete product origination activity the online channel--meaning every loan product under the sun will share common screen designs and workflow configuration. Another bank, far smaller, has taken a approach to synchronize data between the online and ATM channels so that, the words of a consultant, a customer could reset ATM interface preferences from a home computer, as an example. TD BankNorth, according to an executive at the Halifax, Canada, IT consulting firm Keane, Inc., has relied on web services, a predecessor form, part to enact a speedier conversion process post merger. Citibank has gone on the record at a recent Burton Group Catalyst Conference, describing its SOA infrastructure roadmap plan. Several more institutions have similar integration projects the works, and this is just the start of what could be the next fundamental shift how automation gets built. And yet, misunderstandings about the technique and tools prevail even though many the banking industry are taking great strides with SOA the last 18 months, and at least 80% of the top 50 banks are doing something with the method, according to sources. One root issue is whether a bank question emphasizes the services aspect of the technology (what it can do to modernize business process for clients and employees) or the architecture aspect, that is, the idea of code becoming uniform and reused. latter scenario, obviously, is more of a geeky, IT kind of initiative that might leave business people not understanding what value they've gotten. What makes a service SOA recap, case you've missed the basics elsewhere: are more like a method of computing and software design than a technology like, say, Linux. Think of it as a concept about how business logic is supposed to be structured with a decidedly pragmatic implication for business agility and development speed. A service is somewhat analogous to a mini program and it does some fundamental unit of work; moreover, it's designed to be reused and recombined with other mini programs to build what's referred to as a composite application. Services really are like today's Legos with multiple custom shapes, says Jim Adamczyk, senior executive, Accenture, New York City. The pieces aren't the uniform blocks of my youth, yet each piece still has that same great uniform connector that makes it easily linked to get work done. consultant and IT expert was talking toys to make a bigger point, that SOA is, the view of many, a logical extension of web (discussed here and elsewhere between 2000 and 2004) built with WSDL, SOAP, and other standards that yield interoperability. Where web were humble and relied a lot on the imagination of the handler--just like those old-fashioned plastic blocks hunter green, bold blue, and siren red--next generation have more specialty and shape out of the box. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.009
Scholarly communication0.0110.019
Open science0.0010.006
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0140.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.221
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueABA banking journalSame topicService-Oriented Architecture and Web ServicesFrench-language works237,207