Even thought TIBCO’s BusinessEvents does not yet support Bayesian Classifiers, Artificial Neural Networks and other advanced decision support algorithms, it is just a matter of time before TIBCO will add these advanced features “out of the box”.  On the other hand, the extensible nature of TIBCO’s BE makes it possible to add probabalistic computing functionality, however this requires quite a lot of programming and integration work.

When I see a great release like this for TIBCO, it makes me a little nostalgic for “the good old days” travelling the world in the front of the aircraft for TIBCO.   TIBCO has a rich and diverse customer base.  This customer base includes financial services companies; however, TIBCO is much less dependent on financial services than other event processing companies.   So, with TIBCO you not only get great technology, but rock-solid stability in an unstable and uncertain business world.

As a side note, an S&P analyst recently downgraded TIBCO’s stock (TIBX), primarily due to chao in the financial services sector.    Because of TIBCO’s global reach and stability, plus forward vision, advanced technologies and many years of commericial success, the S&P downgrade will create a buying opportunity for TIBCO stock.

Having a validated drug discovery platform is the first and most important criterion for defining a good platform company. The platform is typically comprised of a combination of technology, experienced personnel and intellectual property that can generate a stream of drug candidates. Most importantly, investing should be done only after a product of the platform demonstrates activity in clinical trials. Having a clinically validated product is not a guarantee for future success of the platform nor does it mean that the specific agent will reach the market, but it does imply that one or more of the platform’s products stand a reasonable chance of becoming a commercial drug. A validated platform may increase overall success rates, yet the odds of a particular drug candidate to make it all the way to approval are still low.

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Exelixis is active in the ever growing market of kinase inhibitors (KIs) for the treatment of cancer, that is, drugs that block the activity of kinases in cancer cells. Cancer cells are often described as cells that are out of control: They proliferate quickly, ignore death signals, invade nearby tissues and eventually metastasize to distant organs. These disease onset and advancement are associated with processes such as cell growth, motility and blood-vessel formation, which are governed by a complex network made of kinases. Thus, blocking these processes by inhibiting the relevant kinases has emerged as one of the most attractive approaches to fighting cancer.

Together with monoclonal antibodies, kinase inhibitors represent a paradigm shift in cancer treatment from cytotoxic agents to targeted therapies, a trend that is constantly growing. Like antibodies for cancer, kinase inhibitors target tumors while sparing healthy cells and consequently lead to better activity with fewer side effects. Kinase inhibitors, however, possess several advantages over antibodies. The most evident advantage is that KIs can hit targets inside the cell while antibodies can only bind targets presented on the cell surface, so internal targets are approachable only by KIs. Another advantage is the fact that KIs can be given orally, which is a major factor in terms of patient convenience, especially given the typical long treatment duration associated with targeted therapies. Another advantage, which will be later discussed in the article, is the ability to produce KIs that hit several targets at once.

The Anchorage Daily News reports back in September 2004:

Sarah Palin never thought of herself as an investigator. Yet there she was, hacking uncomfortably into Randy Ruedrich’s computer, looking for evidence that the state Republican Party boss had broken the state ethics law while a member of the Alaska Oil & Gas Conservation Commission.

The next week, when Palin went back to work at the AOGCC, she noticed that Ruedrich had removed his pictures from the walls and the personal effects from his desk. But as she and an AOGCC technician worked their way around his computer password at the behest of an assistant attorney general in Fairbanks, they found his cleanup had not extended to his electronic files.

The technician “said it looked like he tried to delete this, but she knew a way to go around and get some of the deleted stuff,” Palin said in an interview. “I didn’t know what I was looking for, but I was there.”

And this is how Salon reports the same incident:

“In a neat symbolic fit, the agent responsible for Alaska’s current moment of reform and modernization is a woman, a breed once nearly as rare in far Northwest politics as a Democrat. Sarah Palin, a libertarian and hockey mom from the fast-growing suburbs of Anchorage, began her political career — as an appointed member of the state’s Oil and Gas Commission — by hacking into the computer of another commissioner, Randy Ruedrich, chairman of the Alaska Republican Party. Palin was seeking the evidence that she would eventually use to charge him with an improper relationship with lobbyists. (Ruedrich would later settle state ethics charges against him by paying a $12,000 fine.)”

Is this where the McCain administration is going to get their computer security expertise? She’s not a security expert but it is nice to see someone at the level of state govenor who knows their way around a computer.

adaptive process management (n.) an element of resource and business process management, adaptive search and event processing. Sometimes referred to as “Level 4” event processing or process refinement.

application concept (n.) a definition of a set of properties that represent the data fields of an application entity. An application concept can describe relationships among themselves. For example, an order concept might have a parent/child relationship with an item concept. A department concept might be related to a purchase requisition concept based on the shared property, department_id. Application concepts can include an application state model.

application state modeler (n.) a UML-compliant application that allows you to model the life cycle of a concept instance — that is, for each instance of a given concept, you can define which states it will pass through and how it will transition from state to state. States have entry actions, exit actions, and conditions, providing precision control over the behavior of an event processing agent. Transitions between states also may have rules. Multiple types of states and transitions maximize the versatility and power of the application state modeler.

derived event (n.) an event that is created as a result of processing one or more other events.

complex event (n.) an event that is a situation-entity abstraction of two or more simple, derived or other complex events.

complex event processing (n.) CEP is a technology for extracting information from message-based systems. CEP is primarily an event processing concept that deals with the task of processing multiple events from an event cloud with the goal of identifying the meaningful events within the event cloud. CEP employs techniques such as detection of complex patterns of many events, event correlation and abstraction, event hierarchies, and relationships between events such as causality, membership, and timing, and event-driven processes.

event (n.) a instance of an event definition. It is an immutable object that represents a business activity that happened at a single point in time. Just as one cannot change the fact that a given activity occurred, one cannot change an event — events are immutable.

event aggregation (n.) the aggregation of simple, derived or complex events into higher levels of event abstractions.

event definition (n.) a set of properties related to a given activity that represents an important or interesting change of state in a human, system or computational activity. An event definition includes event properties such as event priority, event time to live (TTL), and a description of the payload, which is comprehensive information related to the activity that occurred. Events expire when the TTL has elapsed, unless the event processing agent has instructions to consume them prior to that time.

event channel (n.) a communications channel in which events are transmitted from event source to event receivers, typically received as electronic messages. Each channel can have multiple destination and. events can be configured to transmit to a default destination. JMS is an example of an event channel.

event cloud (n.) a partially ordered set of events (poset), either bounded or unbounded, where the partial orderings are imposed by the causal, timing and other relationships between the events. Typically an event cloud is created by the events produced by one or more distributed systems. An event cloud may contain many event types, event streams and event channels. The difference between a cloud and a stream is that there is no event relationship that totally orders the events in a cloud.

event-driven (n.) the behavior of a human, system or computational entity whose execution or actuation is in response to events, typically received as electronic messages.

event-driven architecture (n.) an architectural style for distributed computing applications in which some of the components are event-driven and communicate by means of events.

event processing (n.) computing that performs operations on events, including modifying, creating and destroying events.

event-object (n.) an software object that represents an event, generally for the purpose of computer processing, that exhibits both encapsulation, inheritance and polymorphism.

event prediction (n.) computational activity where the impact of events, complex events, and situations caused by events identified, including both opportunity or threat. Sometimes referred to as “Level 2” event processing, impact assessment or predictive analytics.

event pre-processing (n.) computational activity where events are cleansed or normalized to produce semantically understandable data. Sometimes referred to as “Level 0” event processing.

event processing (n.) computational activities on events dealing with the association, correlation, and combination of event data and information from single and multiple event sources to achieve refined identity and situation estimates for observed event objects, and to achieve complete and timely assessments of opportunities, threats, and their significance. Event processing is characterized by continuous refinements of event estimates and assessments and by evaluation of the need for additional sources, or modification of the process itself, to achieve improved results.

event processing agent (n.) an EPA is a computational entity that performs event processing.

event processing network (n.) a set of event processing agents and a set of event channels connecting them.

event properties (n.) data representation of an event, typically by name-value pairs of type string, integer, real, boolean or a complex data type.

event refinement (n.) filter, identify and track events & make initial processing decisions based on association, correlation and state estimation. Sometimes referred to as “Level 1” event, or event-object, track and trace.

event stream (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

event stream processing (n.) a time-ordered sequence of events. An event stream may be bounded by a certain time interval or other contextual dimension (content, space, source, certainty), or be open ended and unbounded.

rule (n.) defines what triggers unusual, suspicious, problematic, or advantageous activity within an event processing agent and what the EPA does when it discovers these types of activities. Rules execute actions based on certain conditions on events, instances, or a combination of both. A rule includes a group of condition-rule statements and action-rule statements. The condition statements instruct the EPA what to look for in events, and action statements instruct the EPA how to respond when conditions are met. If all the conditions in a rule are satisfied by events or instances or both, the EPA fires the actions. The action might be to execute tasks, create an event instance, modify property values in an event instance, create and send an event, or something else.

rules engine (n.) a type of event processing agent that uses a declarative programming model to process events. Formally described as “an abstract structure that describes a formal language precisely, i.e., a set of rules that mathematically delineates a (usually infinite) set of finite-length strings over a (usually finite) alphabet“. Informally, it can be any system that uses rules, in any form, that can be applied to data to produce outcomes.

rule language (n.) is an artificial language that is used to control the behavior of an event processing agent. Rules languages, like human languages, have syntactic and semantic rules to define meaning.

situation refinement (n.) identify situations, or complex events, based on event clustering, event-event relationships and relationship analysis and context. Sometimes referred to as “Level 2” event processing.

simple event (n.) an event that is not an abstraction or composition of other events.

virtual event (n.) an event that is imagined, modeled or simulated.

One of my favorite banks in Thailand is K-Bank. With K-Bank I can simply walk up to an ATM machine and pay a mobile phone bill, purchase mutual funds, buy insurance, or transact an ever-growing list of services payable at the modern and sleek K-Bank ATM.

For example, tomorrow I fly to Chiang Mai in Northern Thailand and found K-Bank’s service amazingly better than in the US. For example, I booked my flight as usual (over the phone, but could have used the Internet) and told the reservation agent I was going to pay by ATM. He simply gave me a PayCode and told me I had three hours to go to the ATM and enter the PayCode to perfect my reservation.  I also got the PayCode via SMS.  This gave me the time I needed to make sure I had booked the perfect boutique hotel in Chiang Mai, the Fern Paradise.

Then, I went out into the beautiful Thai weather and completely my airplane reservation at the ATM machine; which also printed out a receipt with my flight details and reservation number.

It sometimes amazes me how much further advanced some services are in Thailand compared to the US. To me, it feels more secure not to use an on-line payment center or give out my credit card details over the phone. I can simply book a ticket, take a PayCode, and complete the transaction at a nice modern, shiny, K-Bank ATM machine.

Who knows, maybe soon I can select the perfect window seat at the ATM and the receipt will act as my boarding pass!