The Event Model TEM provides a new way to model, develop, validate, maintain, and implement event-driven applications. In TEM, the event derivation logic is expressed through a high-level declarative language through a collection of normalized tables Excel spreadsheet like fashion. These tables can be validated and transformed into code generation. TEM is based on a set of well-defined principles and building blocks, and does not require substantial programming skills, therefore target to non-technical people. TDM groups the rules into natural logical groups to create a structure that makes the model relatively simple to understand, communicate, and manage.
For example, in Table 1, the Pattern condition describes an absence detection of both Cash deposit and Transfer abroad events, Liver enlargement treatment means that no event Temporal event model tem from these two events is detected within the specified context. The Promise of the Future With The Event Model, there is a diagram dedicated to event eveng and supported with corresponding event derivation tables. See more articles by Barbara von Halle. Figure 8: Relation between a block in the diagram with its corresponding EDT for Cash deposit followed by transfer abroad derived event. But, most important are the lessons these models taught us that we now take forward into the world of event modeling. Fournier has over fifteen years of research, practical experience, and numerous publications in the areas of event processing, organizational and process modelling, business transformation, and management.
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Philosophical logic. In a development that foreshadowed a similar one in computer science, Prior took this under advisement, and developed Temporal event model tem theories of branching time, which he called "Ockhamist" and "Peircean". Hidden categories: Pages using Timeline Wikipedia articles needing clarification from April Commons category link from Wikidata. Re-reading your own work, especially at some temporal distance, is a dangerous business. A unique ID field that exists in both components is used to join the two and create a complete picture of each event's information. In the case of fixed-time data, these components may appear in separate files Fudge packer picture tables. This presupposes an environment that may act unpredictably. Fixed-time data containing simple events can be organized in one table, which includes the date and any other attributes that are Temporal event model tem. Time and modality: the John Locke lectures for —6, delivered at the University of Oxford. Reasoning Web. Modalities and Multimodalities. The input feature class must always contain the geographic features and the ID to join to the input table. Retrieved
That goal was to improve the current event processing paradigm with a model similar to The Decision Model.
- Naturalism tells us that mystics had temporal lobe epilepsy.
- Temporal data includes information about temporal events, which describe an observation or a set of observations of a particular object or group of objects.
- In logic , temporal logic is any system of rules and symbolism for representing, and reasoning about, propositions qualified in terms of time for example, "I am always hungry", "I will eventually be hungry", or "I will be hungry until I eat something".
The Event model TEM is a novel way to model, develop, validate, maintain, and implement event-driven applications targeted at business people. This is the second article about TEM. Our first article  answers two fundamentals questions: Why TEM? In this article we address the question of How TEM? For those who missed the first article we hope that you will still find this article useful and self-contained in such a way that it will be easy to follow and understand.
According to Forrester, complex event processing, or CEP, combines data from multiple sources to infer events or patterns that suggest more complicated circumstances. The ultimate goal: to identify meaningful events, such as opportunities or threats, and respond to them as quickly as possible.
Luckham Dec points out that companies that implement active, continuously-running real-time intelligence systems will leverage CEP because it is the only way to extract insights from current data in an event-driven manner. The use of CEP will expand further as the pace of business accelerates, more data becomes available in real-time, and business people demand better situation awareness.
The question is: why is the adoption of event processing tools so low? We believe that to really leverage the power of events, the critical piece of the puzzle that must be solved is how to make event driven applications easier for non-technical people to understand, easier to develop, and easier to deploy.
TEM is intended to exactly fill in this gap. TEM is primarily targeted at a non-technical audience who wants to gain control over the event logic in their organizations. As you will see later on, a non-technical user should be able to specify the event business logic of an application in TEM, while the technical details can come at a later phase, from which the model is generated into code.
In this Part 2, our main objective is to walk through the essentials of TEM using an illustrative example that demonstrates the top-down design approach. Therefore, included in this top-down approach are the TEM pieces most useful to the non-technical audience. In Part 1 we introduced the situation called a Suspicious account.
A situation in TEM is an ultimate event-based conclusion or derived event that is emitted to the outside world. In our first example, A suspicious account is derived whenever there are at least three large cash deposits every 10 days. A situation of a Suspicious account is derived when any of the following three derived events is detected Figure 1 : Frequent large cash deposits; Frequent cash deposits followed by transfers abroad ; or Lack of account activity.
We derive a Frequent large cash deposit event whenever we have at least three Large cash deposits every 10 days for a certain account. A Large cash deposit event is derived whenever the amount of the cash deposit is larger than the amount allowable for the customer customer threshold.
A Frequent cash deposits followed by transfers abroad event is derived whenever we have at least 10 Cash deposit events followed by a transfer abroad event every 30 days. The latter is derived whenever we detect a Transfer abroad event that occurs after a Cash deposit event for the same account and that the cash amount is larger than the transfer amount, for the same account.
The temporal window starts with a Cash deposit event for a period of 3 days. A Lack of account activity event is derived whenever, for a specific account, there are neither Cash deposit events nor Transfer abroad events in a period of 20 days starting with the occurrence of either a Cash deposit or Transfer abroad event.
Finally we derive a Suspicious account situation whenever a Frequent large cash deposit , a Frequent cash deposits followed by transfer abroad , or a Lack of account activity events are derived. TEM Glossary : The set of tables that stores all glossary concepts of a specific application.
TEM Diagrams : The set of diagrams that describes the events dependencies, and hence the event flow, in an event-driven application. TEM Logic : The set of tables that describes all logic concepts of a specific application.
The ultimate goal of a TEM model is to be translated into a running application with minimal IT intervention. TEM principles enable validation of the model so it is correct, complete, and consistent and can be automatically translated into an event processing running application. Although a correct, complete, and valid model requires the completion and specification of all these building blocks, as we will see later on, the most relevant artifacts for a business user in TEM are the diagrams and the logic tables.
These can be defined first by non-technical people, while the other artifacts can be specified in parallel or afterwards by business architects or more technical people. In TEM, an event is an occurrence of something that has happened that is of interest to a business and is published so that we can detect it. A raw event is simply the raw output of an event producer. An example of a raw event is a cash deposit into a bank account and the event producer is human if a person is doing the depositing or banking transaction systems in most businesses.
A derived event is the output or conclusion of applying event logic on input events. An example is that a large cash deposit has been made into a bank account. The event logic, in this case, contains the criteria for qualifying a specific cash deposit as being a large one. A situation is a final conclusion from an entire event model and it has at least one consumer who is interested in it. This means that it is published to the consumer who can react to it. In our example, the consumer of the Suspicious situation is the Compliance officer of the bank.
A fact is an atomic piece of data contained in any event, producer, or consumer. For example: customer ID or amount in a Transaction event. As seen in Part 1, context is one of the main characteristics and singularities in event processing. Context defines the way we partition or group the event occurrences so these partitions can be processed separately.
When we derive an event in TEM see Section on TEM logic below , we do so by applying conditions or expressions against input events in a specified context. TEM distinguishes between two types of context:. Partition by , that is, we group the event occurrences by one or more fact values. For example: customer ID, meaning we group and process together all transactions belonging to a specific customer. When , that is, we group together the event occurrences based on time or temporal windows.
For example, we are looking at event occurrences that happened in a time window of 10 days. As pointed out, a business user defines or confirms the business logic of an event-driven application by specifying two main artifacts, the TEM diagrams and logic tables. The Event Model diagram is a simple drawing that illustrates the structure of the logic by showing a situation along with the connections of derivations in a top-down manner. At the top of the diagram is a goal which is the situation to be derived.
This goal is connected with the raw and derived events that are identified as participants in the situation derivation. This is done in a recursive way until raw events or facts are encountered as depicted in Figure 3 for our Suspicious account example. There is a set of nine icons to express all the relevant terms and relationships in a TEM diagram see Figure 2.
Each block in the diagram, separated by vertical lines, represents a single event derivation table detailed in the next section. Each rectangle in the block, separated by a thick black line, corresponds to a row in the respective logic table. The red rectangles in the background represent the context for the specific row. The contexts can be collapsed or expanded. In the case of the Lack of account activity derived event, the context is expanded, and so you can see that the temporal context is initiated either by a Cash deposit or Transfer abroad event, while the events are partitioned by the Account ID of the customer.
In our TEM diagram, there are six blocks that correspond to six different logic tables. These will be detailed later on. Dotted lines specify event flows to and from the event-driven system, that is, events flows from producers, the Bank transaction system in our case, or to consumers, the Compliance officer. The TEM diagram is the major design tool that provides a top down view. All blocks that describe situations or derived events require the definition of the corresponding logic concepts.
Figure 3: TEM diagram for the Suspicious account example. Logic concepts are the details behind event derivation. An EDT specifies the conditions for generation of occurrences of a specific event.
Table 1 details the circumstances under which a new occurrence of a Lack of account activity event is derived.
So Table 1 contains the details for the derived event Lack of account activity. The table consists of two parts, context and conditions, separated by a vertical red line. The context part consists of two logical sections. The temporal context, represented by When expression, When start, and When end columns; and the segmentation context represented by the Partition by column.
For example, Table 1 describes a temporal window initiated each time a Cash deposit or a Transfer abroad event occurs and ends after 20 days. The Partition by context groups the input events by the same Account ID , meaning all Cash deposit and Transfer abroad transactions of the same account are processed together looking for the pattern specified under the Condition part.
Each row in an EDT specifies a different set of conditions and context to derive a new occurrence of the derived event at hand. In order to derive a new occurrence of the derived event at least one of the rows must be satisfied, meaning all conditions in the row must be satisfied for the specific context in that row.
The conditions part of an event derivation table consists of three types of conditions. The conditions are logically applied in the following order:. Filter conditions are expressions evaluated against the content of a single event instance. The role of filter conditions is to determine whether an event instance satisfies the filtering condition and should participate in the derivation.
For example, the Filter on event column in Table 2, describes a condition on a fact type cash amount of the event Cash deposit. The cash amount value must be equal or larger than the amount allowed for this customer denoted by the customer threshold fact. Pattern conditions are expressions on related event occurrences, such as, Detected, Absent, or Thresholds over Aggregations. The role of pattern conditions is to detect the specified relationships among event instances.
For example, in Table 1, the Pattern condition describes an absence detection of both Cash deposit and Transfer abroad events, which means that no event occurrence from these two events is detected within the specified context. In Table 3, the Pattern condition is specified by the aggregate Count , and is satisfied only if the number of Large cash deposit events is larger than 3 for the specific context.
Filter on pattern conditions are expressions on multiple event occurrences, including comparisons, memberships, and time-relationships that result after applying the Pattern condition. The role of the filter on patterns conditions is to filter the pattern result based on conditions among the different events that issue this pattern. For example, in Table 4, the pair of events that come in the sequence of first a Cash deposit and then a Transfer abroad , are then filter to test whether the cash amount is equal or less than the transfer amount.
Only those pairs of transactions that satisfy this condition are included in this derivation. The three types of conditions are optional, meaning they can either appear or not in an EDT, however an EDT is valid only if it contains at least one condition per row. For example, Table 1 contains two conditions for the Pattern type of condition.
Connections are shown in TEM tables with underlines or hyperlinks. For example, Large cash deposit in Table 3 is underlined since this event itself is the conclusion of the Large cash deposit EDT.
A simple temporal event contains all necessary information in one message or record, referred to as the temporal observation component. If you are adding stationary events, the input feature class should contain static attributes, but not the date and time information for the events. For instance, one may wish to say that whenever a request is made, access to a resource is eventually granted, but it is never granted to two requestors simultaneously. Categories : Temporal logic. The sentential tense logic introduced in Time and Modality has four non- truth-functional modal operators in addition to all usual truth-functional operators in first-order propositional logic. Temporal logic has two kinds of operators: logical operators and modal operators . Of, relating to, or near the temples of the skull.
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That goal was to improve the current event processing paradigm with a model similar to The Decision Model. This two-part series introduces the result of that joint study. If you are familiar with The Decision Model, the similarities and differences will be well understood. If you are not familiar with The Decision Model, you need not worry. This series will still be useful because The Event Model is meant to be easy to understand.
Even so, already there is a lot of interest in The Event Model. One reason is that the general understanding of events, even in everyday life, seems intuitive to most people more than was the formal understanding of business logic, business rules, and business decisions back in Furthermore, business people understand the necessity to analyze and act on events as they happen. Another reason is that organizations are struggling with event processing as a result of new regulations, the Internet of Things, and the promise of Big Data.
This two part series lays a foundation for a basic understanding of The Event Model. It does not address technology tasks that are needed to deploy event models into executable code. Objectives In this Part 1, there are three objectives. The first is to revisit lessons from past innovations. It may seem unimportant to revisit lessons from past history about similar models. To the contrary, it is quite important because revisiting past successes can crystallize an exciting future.
Moreover, it can pave the way to an exciting vision. You may find it fascinating to be on the edge of research that is new, promising, and needed. The second objective is to introduce the event paradigm. The third is to provide a first glance at The Event Model in preparation for its details in Part 2.
The journey to TEM begins with the timeline in Figure 1. If you have lived during its early years, you understand why it is a powerful timeline. If you have not, you may discover insights that today you take for granted but that are quite intriguing and inspiring. First Time The first important part of the timeline is that the relational model was revealed in a paper by Dr.
Codd at IBM in It was a predefined abstract data model that was technology independent. From this model emerged relational systems as we know them today. In other words, the relational model existed before there was specific relational technology.
Relational systems from IBM appeared in the s and adoption happened later and slowly. Twenty years after , Fleming and von Halle published a book as did other people on how to correlate the various data modeling diagrams of the s with the relational theory from Dr.
Second Time The second important part on this timeline is that, prior to , the business rule space had no universal modeling diagrams or modeling theory. So, von Halle and Goldberg, after testing The Decision Model with clients, published a book that pulled together new decision model theory and a diagramming technique for the better management of business rules and business logic. In , The Decision Model was adopted by a leading financial institution even without commercially available software.
The years saw the spread of decision modeling and supporting enterprise software in global financial institutions. And most recently, in January , the OMG i. This new standard legitimizes decision modeling as a new discipline and new software marketplace. Third Time The third important part of this timeline is the focus of this series. Simply put, we combined a diagram notation with appropriate event-based science while keeping it business-friendly and technology independent.
For the purpose of comparison, consider that the logic in The Decision Model represents the correlation of fact-based conditions to corresponding fact-based conclusions, the latter in The Decision Model are called business decisions. That is, raw input data is evaluated, interim conclusions are reached, and correlations among raw data and interim conclusions lead to a final student financial aid eligibility conclusion.
On the other hand, the logic in The Event Model represents the correlation of event-based conditions to corresponding event-based conclusions, the latter in The Event Model are called situations. That is, raw events produced by sensors, satellites, and systems are evaluated, interim derived events are reached, and correlations among raw events and derived events lead to a final suspicious plane situation.
Five Lessons Learned From History Before delving into the event paradigm, what did we learn from the past? The relational model has stood the test of time and The Decision Model should do likewise.
Table 1 summarizes five lessons learned from this history to apply to the idea of an event model. Table 1: Lessons Learned From the Past.
First, a predefined technology independent model has proven to be valuable. It represents a vision first and supporting technology to follow. Second, a business-friendly structure also has value in separating business representation from technology-specific representation. Specifically, a top-down structural diagram supported by corresponding logic tables has proven to be intuitive and understandable by business people. Third, underlying principles behind models are valuable because they prescribe the structure, keep it technology independent, and define optimum integrity of the content within that structure.
Most importantly, they also provide a means for model validation. Fourth, a solid theoretical foundation of normalization is valuable. It reduces the content of each model to its minimal representation which saves overhead and reduces errors of future maintenance. And finally, model-driven code generation is not only good, it opens the door to many advantages. The Holy Grail is to generate code directly from the model so that non-technical people can create the models or at least understand them , possibly validate them against principles and with test cases, and IT can deploy them to target technologies.
In other words, it closes the gap between business and IT. So while each of these models is different from each other, these five characteristics are common and deliver proven value: technology independence, business-friendly, supported with principles, based on normalization, and amenable to code generation.
What is Situation Awareness? To understand the event paradigm leading to this situation awareness, the left most photo in Figure 2 detects that a person has entered a car and the car has started to move and derives that the person does not resemble any of the authorized drivers.
The second photo in Figure 2 further derives that none of the authorized drivers are in the car and we become aware of a situation of an active car theft.
Figure 2 then decides whom to notify and whether to activate a means for stopping the car. If so, the activated stopper slows down the car and a dispatch notification goes to the security company. This, in a nutshell, is an example of the event paradigm. So, the event paradigm is the processing of events and their data in near real-time, potentially from multiple input sources, to create situation awareness and react to it.
The real-time aspect along with the sense and react of the event paradigm differentiates it from other paradigms. For example, recording events, storing their data and analyzing them later, does not represent the full richness and uniqueness of the event paradigm. Logic applied to these raw events brings us to the second D to derive a resulting event.
A resulting event, called a derived event, is a happening of greater magnitude and importance than simply the individual raw happenings viewed in a vacuum. There can be many derived events before reaching the ultimate event-based conclusion. The ultimate event-based conclusion or derived event in The Event Model is called a situation.
Once a situation is derived, the third D for decide means to determine what to do about it. Once it is determined what to do about it, the final D comes into play, which is simply do it! So the detection of raw input events as input and the application of logic to arrive at derived events and eventually to arrive at the final situation of interest is what happens in The Event Model.
Deciding what to do about it may happen in The Decision Model. The reaction to it may happen in a process model or task. Figure 4 illustrates the inherent complexity in the event paradigm among event detection, timing, and situation awareness, using an example of suspicious account derivation.
First, the situation is driven by events event-driven logic. Third, the situation is valuable and relevant in temporal windows. Furthermore, the situation should be derived as soon as it happens at the end of the temporal window so that the reaction and decision can be made in real-time. Figure 4: Insights into event derivation complexity.
What are Events and why are They Important? Moving from the event paradigm to event basics, an event is simply a happening anywhere in the world — a traffic light changes, a college student graduates, some money is deposited in a bank account and other money is withdrawn from a bank account.
Some events are related to others in obvious ways — a batter in baseball incurs a third strike an event and therefore incurs an out another event. Other events are not so obviously related, perhaps on purpose, such as events leading to money laundering or other fraud. Today events are ubiquitous and more accessible than before because it is possible to harness worldwide happenings and related information through the Internet, operational systems, a wide variety of devices, or other means; and over time.
Applying logic to them to establish hidden relationships allows for the derivation of current, past, or future happenings to react to. In other words, the ability to relate individual events to each other as they happen through logic can lead to a conclusion or situation that is greater in importance and impact than the individual events themselves. These event-derived conclusions or situations may be good, such as upcoming marketing trends.
They may be critical happenings such as pending emergencies. They may also be dangerous such as suspicious activity on a bank account or within a public place. Difficulties of Event Processing Today Just as data existed before the relational model and business rules and logic existed before The Decision Model, events existed before The Event Model.
So, how do people today detect and react to them? One option is through process modeling. This is similar to how people dealt with data, business rules and logic. Before appropriate models, there was no model just for representing data or business rules. It turns out, just as process models are not the most appropriate way to represent data or business rules, process models for event logic is not a desirable solution for four similar reasons:. Another option is to represent event logic directly in program code much as some organizations have done with business rules and logic prior to The Decision Model.
Yet, program code is not an ideal solution for representing event logic for four reasons:. So, essentially, use of program code loses the business audience and their ability to govern the event logic.