- First, event management is primarily about the identification and generation of business events from the ambient events. Similar to what Carole-Ann and I had written in previous posts.- Second, IBM wants to introduce high level EPLs to express the logic for that processing that are business-centric, something very similar to what Business Rules Languages and approaches are in the business rules management area.
- leave anything related to transport, communication to other layers- use this revised CEP to express and execute event-relevant logic, the purpose of which is to translate the ambient events into relevant business events- have these business events trigger business processes (however lightweight you want to make them)- have these business processes invoke decision services implemented through decision management to decide what they should be doing at every step- have the business processes invoke action services to execute the actions decided by the decision services- all the while generating business events or ambient events- etc.
On Event Processing Agents implies a “new” event processing reference architecture with terms like,
(1) simple event processing agents for filtering and routing,
(2) mediated event processing agents for event enrichment, transformation, validation,
(3) complex event processing agents for pattern detection, and
(4) intelligent event processing agents for prediction, decisions.
Frankly, while I generally agree with the concepts, I think the terms in On Event Processing Agents tend to add to the confusion because these concepts in On Event Processing Agents are following, almost exactly, the same reference architecture (and terms) for MSDF, illustrated again below to aid the reader.
'Last week, Friends of Ed. very nicely sent me a review copy of Ira Greenberg’s book Processing: Creative Coding and Computational Art (ISBN: 159059617X).'
Affective Processing of Loved Familiar Faces: Contributions from Electromyography | InTechOpen, Published on: 2012-01-11. Authors: Pedro Guerra, Alicia Sánchez-Adam, Lourdes Anllo-Vento, et
Using RhNav - Rhizome Navigation I wrote a data aggregator for Technorati's API. The first result is a video which visualizes blog domains by analysing Technorati's Cosmos (the blogs which link to a particular URL). The video is a screencast of RhNav fetc
Imagers based on focal plane arrays (FPA) risk introducing in-band and out-of-band spurious response, or aliasing, due to undersampling. This can make high-level discrimination tasks such as recognition and identification much more difficult. To overcome this problem, three-chip color charge coupled device (CCD) cameras typically offset one CCD by 1/2 pixel with respect to the other two. Analogously, monochrome imagers including infrared can use microscan (or dither) to reduce aliasing. This...
BI stands for Business Intelligence, which to some will sound suspiciously similar to Groucho’s famous comment. But in reality BI is more to do with providing the right “Business Information” to people who need it (i.e. business analysts), and there
This reference is either acquired through a stringified URI string, NameService lookup (similar to DNS), or passed-in as a method parameter during a call. Object references are lightweight objects matching the interface of the real object (remote or local). Method calls on the reference result in subsequent calls to the ORB and blocking on the thread while waiting for a reply, success or failure. The parameters, return data (if any), and exception data are marshaled internally by the ORB according the local language and OS mapping. [edit]
JT has posted his view on rules and decisions and how they relate. Given that James talks more about services than events, I thought it would be worth reviewing his post from both a Complex Event Processing and a TIBCO BusinessEvents event processing platform perspective.
”Decision Services:
Support business processes by making the business decisions that allow a process to continue.
Support event processing systems by adding business decisions to event correlation decisions (they are often called Decision Agents in this context).
Allow crucial and high-maintenance parts of legacy enterprise applications to be externalized for reuse and agility.
Can be plugged into a variety of systems using Enterprise Service Bus approaches.”
Most BREs today are deployed as “decision services”, and are used in “stateless” transactions to make “decisions” as a part of a business process. A CEP application is instead processing multiple event streams and sources over time, which requires a “stateful” rule service optimized for long running. This is an important distinction, as a stateful BRE for long-running processes needs to have failover support - the ability to cache its working memory for application restarting or distribution. And of course long-running processes need to be very particular over issues like memory handling - no memory leaks allowed!
Bruce makes an interesting comment on business rules too: that “routing logic in process gateways” are not “business rules”. That doesn’t really make sense: for sure some gateways will be process-housekeeping decisions of little interest to the business user, but others will surely embed business-critical decisions. On the other hand, it has long been acknowledged that a best practice for BPM is to delegate such business decisions to a managed decision service - hence the explicit new business rule (aka decision) task in BPMN 2.0. And,in the CEP world, for tools like TIBCO BusinessEvents to invoke a decision managed by its Decision Manager tool.
Multiple channels and types of events…
… executing in multiple Inference Agents (Event Processing Agents on an Event Processing Network)…
… where Events drive Production Rules with associated (shared) data…
… and event patterns (complex events) are derived from the simple events and also drive Production Rules via inferencing…
… to lead to “real-time” decisions.
The main characteristic to be aware of in these tools is that BE is primarily rule-based (using an embedded rule engine), whereas BW and iProcess are orchestration / flow engines. In BE we can use a state diagram to indicate a sequence of states which may define what process / rules apply, but this is really just another way of specifying a particular type of rules (i.e. state transition rules).
The main advantages to specifying behavior as declarative rules are:
Handling complex, event-driven behavior and choreography
Iterative development, rule-by-rule
The main advantages of flow diagrams and BPMN-type models are:
Ease of understanding (especially for simpler process routes)
Process paths are pre-determined and therefore deemed guaranteeable.
In combination these tools provide many of the IT capabilities required in an organization. For example, a business automation task uses BW to consolidate information from multiple existing sources, with human business processes for tasks such as process exceptions managed by iProcess. BE is used to consolidate (complex) events from systems to provide business information, or feed into or drive both BW and iProcess, and also monitors end-to-end system and case performance.
CEP is intelligent software that is essentially the next step in algorithmic trading – it sifts through market events looking for possible patterns and acts on them. A recent study into banks’ IT spending patterns by consultancy Aite Group, suggested that while budgets as a whole were likely to shrink by 5%, CEP investment remains on an upward trajectory. 36% of respondents to the survey intended to spend more on CEP this year than in 2008.
Adam Honore, senior analyst at Aite and author of the report, says: “We’re still bullish on the potential for CEP across financial services. Once one group successfully deploys a CEP application, word spreads and more technology groups look at CEP to help solve their issues.”
Free or low-cost sources of unstructured information, such as Internet news and online discussion sites, provide detailed local and near real-time data on disease outbreaks, even in countries that lack traditional public health surveillance. To improve public health surveillance and, ultimately, interventions, we examined 3 primary systems that process event-based outbreak information: Global Public Health Intelligence Network, HealthMap, and EpiSPIDER. Despite similarities among them, these systems are highly complementary because they monitor different data types, rely on varying levels of automation and human analysis, and distribute distinct information. Future development should focus on linking these systems more closely to public health practitioners in the field and establishing collaborative networks for alert verification and dissemination. Such development would further establish event-based monitoring as an invaluable public health resource that provides critical context and an alternative to traditional indicator-based outbreak reporting.
About a month ago I promised to make some tutorials about Digital Compositing using Processing. Finally I found the time to write an introduction and create a first example.
About a month ago I promised to make some tutorials about Digital Compositing using Processing. Finally I found the time to write an introduction and create a first example.
Folks who have been in event processing fields like network management (NMS) or security management for many years have a very high expectation for processing complex events. Most of the network and security management platforms on the market have basic rule-based processing available “out of the box” and most of these platforms have had the capability to process events in near-real time for decades. Adding a new “rules-based event processing platform” to the network and security management software mix does little to add any additional capability and certainly does not solve any nagging complex detection problems.
Markdown is a text-to-HTML conversion tool for web writers. Markdown allows you to write using an easy-to-read, easy-to-write plain text format, then convert it to structurally valid XHTML (or HTML).
This course is about scalable approaches to processing large amounts of information (terabytes and even petabytes). We focus mostly on MapReduce, which is presently the most accessible and practical means of computing at this scale, but will discuss other approaches as well.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
As I was reading the Smart Roads. Smart Bridges. Smart Grids. feature in the WSJ, I couldn't help but notice all the event processing scenarios and patterns. As you would expect, all the scenarios start with sensors and instrumentation and involve event (instrumentation) transmission, detection and processing. The order, frequency and end actions of transmission, detection and processing vary by scenario. What follows are excerpts from the article in the domains of smart transportation and grids and my associated event processing observations.
Esper and NEsper enable rapid development of applications that process large volumes of incoming messages or events. Esper and NEsper filter and analyze events in various ways, and respond to conditions of interest in real-time.
Various of EP applications are mentioned besides algorithmic trading are: external surveillance by regulators, risk management, auditing, market depth analysis and more.
Function answers the question --- what is being done?Technique answers the question -- how something being done?
Application answers the question --- what is the problem being solved?
ExamplesBusiness Activity Monitoring (BAM) is an application type, it solves the problem of controlling the business activities in order to optimize the business, deal with exceptions etc...Business Rules are type of technique --- which can be used to infer facts from other facts or rules (inference rules) , or to determine action when event occurs and condition is satisfied (ECA rules) and more (there are at least half a dozen types of rules, which are techniques to do something).Event Processing is really a set of functions which does what the name indicates -- process events --- processing can be filtering, transforming, enriching, routing, detect patterns, deriving and some more.
Actually the conceptual model of EPN (event processing network) can be thought as a kind of data flow (although I prefer the term event flow - as what is flowing is really events). The processing unit is EPA (Event Processing Agent). There are indeed two types of input to EPA, which can be called "set-at-a-time" and "event-at-a-time". Typically SQL based languages are more geared to "set-at-a-time", and other languages styles (like ECA rule) are working "event-at-a-time". From conceptual point of view, an EPA get events in channels, one input channels may be of a "stream" type, and in other, the event flow one-by-one. As there are some functions that are naturally set-oriented and other that are naturally event-at-a-time oriented, and application may not fall nicely into one of them, it makes sense to have kind of hybrid systems, and have EPN as the conceptual model on top of both of them...
I believe that the general consensus among those who study this kind of thing, is that any decision made wholly by a computer is an operational decision, even if it affects the behavior/tasks of many people or sub-components. Online decisions, being a subset of automated decisions, would then be operational in nature.
A. Bogoni, W. Xiaoxia, I. Fazal, и A. Willner. Optical Fiber Communication - incudes post deadline papers, 2009.
OFC 2009. Conference on, стр. 1-3--. (2009)
A. McAndrew, и A. Venables. SIGCSE '05: Proceedings of the 36th SIGCSE technical symposium on Computer science education, стр. 337--341. New York, NY, USA, ACM, (2005)- Kuvaprosessoinnin osa-alueita opetettiin yläkoululaisille: kvantisointi, kohinanpoisto, yms.
- Oppilaat tyytyväisiä
- Ei syytä miksei voisi opettaa jo ennen undergraduate/post-graduate tasoa.
C. Paris, N. Colineau, и R. Wilkinson. HT '09: Proceedings of the Twentieth ACM Conference on Hypertext and Hypermedia, New York, NY, USA, ACM, (июля 2009)
C. Janeczko, и H. Lopes. Proceedings of the 2000 Congress on Evolutionary
Computation CEC00, стр. 373--378. La Jolla Marriott Hotel La Jolla, California, USA, IEEE Press, (6-9 July 2000)
M. Schwab, R. Jäschke, и F. Fischer. Proceedings of the 6th International Conference on Natural Language and Speech Processing, стр. 99--109. Association for Computational Linguistics, (2023)