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what engineers the capabilities the breath and stuff without hopefully losing anyone that going to collapse is going to quite intense they're always looking go to sleep you can get a ruby session well Grossman okay so let's get started so drools it's made up currently of Omega this gun that which is about these server-side management's or bring all the web stuff the operations expert is now the rule engine side is the the logic side fusion is poorly event processing side and flow is we start off a thing called a rule play and it became quite quickly obvious that the rules and workflow sits side by side so just pass it down because something I will just pass the dorky down thank you as if flow is extend that now to full-blown isotopic stays drilled expert quick examples little deeper rules blow a very very small natural flow that have much time back on the drawers expert and discernment timers calendars parents treat medicines and drills fusion if we get time see how it goes right so back and start the book class applicants everyone should understand what is I put the guests and setters in there we have a isn't a valid age it's simply saying where the applicant is age less than eighteen then we set the value equal spots very simple everyone's done so rules are all that for website is matched when we recognize a set of data we can execute on that draws has as we've gone beyond just rules we start to include workflow start include class definitions we needed to build a generic APRN knowledge all these things are uh knowledge and you're building knowledge definition so your rules your workflow your class definition we call type declarations or all types of definitions so you have a knowledge builder to build these definitions so first thing you do is created on is build up and then you just keep adding different resources these resources so that's say this is the rule for a resource pack of definitions rules if you wanted to load in a workload the only thing that would change is that a go to drf apply so we got a generic api to lines and build lots different types of much simple way to do the validation and then you just build up the knowledge base the knowledge bases the compiled X could perform all this knowledge one of the things that difference for sweet you do not choose the engine which executes something you build your knowledge base and then actually from top that's where they're executing the flow engine with rule engine was all taken care of visibly we have this is spring integration so this is the XML equivalent of what you just saw there so what is it doing is saying M resources of a rule flow type from that particular URL it's also adding in a decision table it could also add in the Rufus and build up a knowledge base of all these different types of knowledge is all compiled into one executable form we then build sessions from these so the sessions is the short-term way to operate on that you can think of in process terms of process definition the process instance in rules terms it's the rule base and the working memory we use the word sessions for a generic way of working with this once you have a link within spring you can then get camera integration for out-of-the-box services some work with the moment so camera allows you to shave together what they call processes take those up to web services HTTP P and then you can channel those into your case session ones allows you to get declarative session out-of-the-box rules workflow whatever and there's just specifies the type of incoming data transformation it's that simple running behind I'm really so let's go a little bit more on the example you saw you saw the how you the definitions and build definitions next QT this is a stateless session stateless means that it is something that executes once is thrown away it's like a function you give it the data and execute and when it's finished executing it returns so it's very simple you get the session you get your data you were just doing a little bit assertion you're just checking the date before we execute it on our session I are working memory or our process instance we execute on the session that returns and now we can check the values this is a very simple way quite common in validation mortgage applications insurance of processing your dancers they're going to people with complex now you have a room your sprinklers you have a fire and alarms this is for building your definitions for a fire control system so the first rule when there's a rule listen when there is a fire term sprinklers on so there's a fire these are bindings but they give you a reference to a field a reference to the object and we make a join between the fire and sprinkler come on tomorrow's later on and and it's false sprinklers not on return the sprinkler on telling the spring of the room number notification when the fire is gone turn off the sprinkler so what we have now is the difference there's honest else on it's true and we're checking here when there is no farmer the fire does not exist then we turn the sprinkler off I will come out a bit more for those who have been confused about the bindings of stuff in a minute so not only doing toe sprinklers on off for a room we want to be able to raise alarms now we don't want to have an alarm but every time as a foreign room so we have said we saw I'm not sick when there's no fog that exists so when there is one or more fire doing alarm but only good for first time there's a fire and the same when when there are no more fires in the building turn the alarm off retract it's it's a way of being on to do with collections it's cool first order logic for anyone who's in academic terms and finally when there are no alarms and when no sprinkler is on seraing is okay this would just basically for these different rooms put them into the working memory I will put these slides online they'd have time to go through it line by line so basically that's all the data into the session into the working memory and cause fire rules that's paid when you put stuff into the engine especially the rule side the the calculation is done then that Nunley consequences know the actions are executed until you tell it to here and basically runs through and test everything is okay if we then create a fire and we face to tell it there is a fire kitchen fire and a fire in the office and say firewall rules raise it on time sprinkle on for the kitchen room turn the sprinkler on to the office if we then turn the remove the fire from kitchen roof with Barabbas and the tents go again turn the sprinkle off for the obvious term sprinkler that should be opposite of the castle alarm everything is okay so what you have those example of a validation system first of all in mortgage or insurance type applications they have a monitoring type system based alarm so let's get a little deeper counts cash flow accounting period so I told you this is going to be quick by the way so you're going to have to concentrate it's a lot to do what you're going to do is go debits and credits for an accounting period for a given account these are all simple examples I'm going to start with s curves that help you understand how the engine actually works so select staff account period so set up accounts cash flow account period these are your tables these are the data we haven't joined with account number of the cash flow because the account number of the accounts it's a standard SQL everyone initially understand this we're checking for when this is credits I'm a good date range to correlate the select to a given quarter what we're doing here is we're creating a view this is effectively a view we're going to create a view that gives us the cash credits black quarter we then create corresponding view but the cash flow debits for that quarter as you know reviews rose materialized in the view based upon the data to the tables so if we have the data these tables populated with these fields so these rows of data these views with two rows there and one row that the two credits and one debit if we were to have triggers only the views each row each materialized row is going to execute this trigger this is just going to increase the balance this is just going to decrease the balance the end up the balance minus 25 now actually you couldn't do this on a database because databases have a problem called mutating tables where you can't change they were due for select run but it is imaginary well this would work so one's a rule you already seen the format you have rule name quotes optional if you don't have spaces gently quotes some attributes which control the rule execution behavior you have the left-hand side which is the web when sign happens you have a then side which is the right-hand side when site happens then do this then do these actions so so there's a left-hand side where the person equals mark there's the right-hand side prints hello mark what's the difference between this and the rule methods must be called directly you pass specific isn't it it is imperative do this now do this now with this data it is a command rules can never be caught directly in a view you cannot put data into a group into a view you can't say I want this data in this view that's right you have to put it at the table in materializes into the into the view this is actually the same thing you put take data into working memory and it materializes into the view into the rule so rule is like a view count past Pacific instances because you put it into the working memory and then that is materialized into the view so Rio typical patterns a pattern is of the type object type so you have tables in SQL you have classes in Java in rule engines you have object type and this is a very simple one your the object type which forms a pattern and this is made of one or more fill constraints and fill good strength because this is a field name and a restriction values about this is very very simple pattern that's the foundation to this so now we're going to take the simple SQL and we were converted to a rule everyone who now understands how rule engines or databases work how these work how the views calculate across products when you have the joins standard this glove theory should help you understand rule engine so this is doing the credits so this is saying select star from accounting period select star from account the difference is rule engines are a superset of SQL they are more powerful SQL one of that power comes from bindings eighteen eighty see this gives you if you were to select from your row in your table and you can create a variable which can point to a column in that row that's what this is doing to say select star from account period and ap- ility reference time then it says select star from accounts and it creates it finally both on the whole objects and from a field and it's a slip star from the cash flow has a literal constraint type of credits there's our first joint so where cash flow account number equals C that says dollar account number so we got there that account was on his field that's like saying where's Lex does actly the sames this there and the execution semantics are exactly the same desk you up and there's our date range with data is greater than eighteen of start and data's s AP n this is the second same way of doing those two things there so here we have our credit rule and our debit rule we have the data produces two roads there one Rover in a rule engine we call the materialized row we call an activation so an activation is the rule plus the matching data so here we have two activations here we have one activation those activations will fire expertise is consequences which completes the balance decrease the balance that's minus 25 or simple style you're going to show you few more patterns I'm not going to go over these in detail but this is a literal constraint variable constraints multi restrictions as multiple restrictions on that this is just combined to get some more complicated because you can start to expressions these beyond the slides you can come at them later and it gets further complex still believe it or not this thing we're quite proud of because if you were to use whether it's Jess or whether it's Eclipse I log no one has a rule engine that is expressive as this that can have all these ends of pause working with nest excesses maps and rays for someone who worked with Java JavaScript just like you I give absolute I'm actually rule engines they're still back in the 1990s one of them is we're doing drawer just dragging the system it's saying that Java developers in and work with agreement verbs can work with sites doesn't feel like it's you know build some less bloody other systems are very quickly what is a rule engine more employees of production rule system because actually you can have a little JavaScript engine in this validation that's a rule engine so to classify we are an expert system you have different types of expert systems we are on a production rule expert system we were actually work with becoming a hybrid one both the production rule sister and the backward chaining system product line but that's our research you have a production memory that's your rules give views you have your working memory let's have your tables and you insert update retract into your tables then there is a process which takes the tables and rules and then combines them together to create an agenda which in this show segment that's basically how a view would work materializing the road and do your tables so table table table object type object type object type - views - rules now this is the bit that makes a little difference if you would be able to have one view that can aggregate all your other views so amazing so this is a list of all my views this is what this does it basically says I've got two rows in my credit rule I have two rows in my edible two activations white activation the agenda will have three activations on adawn on which they fire so I here I've set this other bring learner to the four gap you can control the execution behavior and set the app salience here it's 50 salience as a form priority default is zero so all the Deepak rules for credits and debits all on 0 they all want to call them in conflicts and because they are all the same level of priority they can be execute arbitrary rules are the more arbitrary you can get execution the better you do not want to have imperative controls in your code you want to try and limit that I know it's this one here we clearly can't Prince balance until the calculations are done so we have to give some controller so if I do - 50 we say do these three rules first don't care which order those three rules happen which as long as those three rules happen together then you can Prince balance so that's conflict resolution wassailia very quickly together the mutating tables issue we have a two-phase system and long ago there's too much but it means that in Java lab you you populate with tables you populate your object types in certain what if I retract built up the agenda to build an agenda of all your possible activations then a ghost agenda based upon the conflict resolution strategy which you just saw pops the first one off and it goes into the consequence to evaluate the consequence when it's back in the consequence then your right hand side is back at the working memory action phase say well in set mode by retract so that means if I was to put some data in and it created a hundred activations and the first one was popped off to the agenda it was evaluating they changed some data the member that 99 there's other activations are no longer true they are to be taken of agenda and that's the last rule engine to mutation single issue and make sure that no rule fires that's not true so that means that just because something is true and activate doesn't mean necessary fires it has to be true when it attempts to fire okay to gutturals flow this is another way of controlling execution plate by simply here's a way of saying when a rule is allowed to fire so there's a declarative format to give procedural control when rules are allowed to fire so rules will say when this is true do this it has no idea of where now or order so you can be good as rule orchestration so we had a new attribute rule for a group calculation that means that this rule may be true they still come fire until the process engine gets to point here and so the pressure than the guests are there it can't find when it gets there it allows it to fire so you learn as the rules to have control of the work for the control of the rules we have many more ways in which the rules and processes interact just too much to do today so just highlight in there there's a rule for a group here's a number against example that combines processes with rules and then you can see side-by-side processes and rules there because we actually down the rules controlling processes as well and we have all sorts of stuff why don't you get the time one can keep say where we get withdrawals is we're not being a ruler anymore or Prosenjit because we rules and processes and I've been processing and semantic mythology's they do not live on their own and each vendor obviously starts from a base and they try and grow that and everything else is quite weak so about three years ago we took a start and said no we're going to have rules process reference all this first class systems we need to define a generic API to mix two work together so that one hasn't does not perceive is stronger than the other and we want to make sure that we can work at a range of modeling techniques so you have the typical solar which is decision services where you're ruling generally present you're completely decoupled and one will call out the other typically the rule engine is in a stateless format you still early on validation calculation this is the simplest that's why service against the soda vector because people couldn't get more complex more than working that doesn't mean there isn't Manning the complex modeling just means assistance happening good enough to make it possible so drools now to go right back to the other end as well to work with very tightly coupled rules and processes and we allow you to work any end of the spectrum and this what we called behavioral modeling it's about taking application looking behavior ammonia we do not make you go processor-intensive rules orientated you use the software to solve a problem the way in which you want to solve this we recognize that when you work with rules press event processing they all follow the same life cycles you design you simulate your tests integrate your camera you deploy exp you audit you have human task of Direction momentum human time human interaction human target Direction is not just a process thing it's also a rule thing you have to start thinking about things in a different way at the moment because BPM is the big baby everyone tends to just shut going to be getting rules and after four so you have to think differently to get these systems we have fully integrated debugging and auditing what that means is that when the rules when the processes are executing we capture your venting system everything asked us mitts event we start process when your extra node when the exit node when you're in certain agents the working memory when you fire a rule with all of its events if you did collect all his events you can create a correlated lock of causality causality means what cause something to happen if I have this rule does something and it causes this process to start if this process is executing it cause these rules to fire if you're working with sarbanes-oxley if you're working with anything where companies will have dedicated people just just correlated reports and I have some of these jobs to do now so which one stairs views our software you get this out of the box you don't have you can apply that guy he's gone we can make them do some useful to be nicer so here you can see the rules things got a little zoom in there we go it's better saying the correlation as well so here the rule is activated the row is materialized here inspired and because it's part of roof a group the ruleflow group has to activate in order for the rule to fires you start to see the correlation between the rules and processes one of the big things we push is domain Pacific processes because rules is all about declarative modeling all about behavioral modeling BPMN and the immigrants who does not cut it one of our areas are peripheries is the medical domain we need to build processes that have language semantics dialogues that we technician that the skill person understands so that means the left hand side icons need to be icons they known words they know the luck where they drag that across whenever the dialogue box has three things that they know so drools makes a trivial to design the main Pacific processes we do name set of processes literally get up and going 50 minutes the meta in file you drop it in and just say this is the icon this is the dialog box Pinterest appears in Eclipse it's preview we have a number of we call word pipe withdrawals as examples so here we take a series of Apache common stuff for automation so this will find files find files on a disk does a forage composite node for each pilot times we'll log it and run some rules to check that's it might be checking dates or whatever it needs to do to decide whether what happens and then uses an archive one so the results of all this will go into the archive we then copy it we email it from the end and if you were to click on one of these the associated dialog box which would pop up from that this is incredibly bored to the scoop you developers but it keeps you the job it means that you're going to work with a business analyst and you're going to say how would you like to capture your knowledge your business on how what is the codification if your business on is your company what do you want your processes to say it's really important so means now you can work we give you the tools to work hand-in-hand to tailor your process development money for your business and then it means they get a tool which does enter working where they can understand because you're honest even be fair to and that were is so low level and your even gets you understand he comes with next but if you are a medical technician you don't understand I need to take a blood pressure and I need to go and do some BP medication it's notify the GP any to be full up and you'll understand the order and the orchestration leaves most of these are workflows are not complex they're not complex and the technicians can understand the procedure honest of these but they can't start with gateways and splits and they're all these really complex stuff so they need icons that they are trained upon they will tell the developer this is what I want my workflow to look like this is what you need to develop so I can get my job done and now I can do something and I can do it quicker I can do it more efficiently I can somebody is trained on we develop these amazing simple processes allows you to create own methodologies which means when you have a new person on they don't have to learn pigment to they just learn the dimension work wear the sunglasses currently a powerful tool and it's not something that anyone addresses in the mark at the moment and I say it's a big growth area to us that people just look uses big organizations do their own work retort because they can't they can't use commercialize can't pave sunders this flexible and when it brings on the sub it costs a lot of money so to have so it's generic there's powerful that comes with world integration everything else we do the absolutely other than we've got an announcement on October where the US Navy health care saw this and osde is I have two numbers on it and they're just like we have to have this and they invested massively into the projects allow us to hire 10 people to work on this side of things so it's incredibly important areas it back into the heart stop I only wanted to touch on purchases so you saw whatnot earlier on just say when I have no red buses these are called conditional elements you have the pattern and then you have this little no here it's like a yeah musical element when there are there are no red buses in my system my session or working memory when there's one or more robust for all when all my muscles are red or when all my buses that have two floors are ready so it's just got a waist looks and sets of data to make decisions based on sets of data typically rule engines in on their list lineage do not work well with Java classes if you work with jet if you work with ilog if you work with this is what they do is you actually have an internal representation it's basically a list array and they will map your classes onto those and it makes almost impossible to work with nested objects so you work with Jess we can work with the strings and you know numbers you can't start to work with more complex object graphs you call them so draws introduces thing called fraud so probe allows you to some pattern is just a type of filter that's what it's doing is filtering the data coming through it so that data doesn't have to come from working memory it can come from anywhere so why can't just say well I've got a person to work for memory match him I've got a binding term and now I can actually filter the results of this expressions this is an ember expression we just evaluate and return to collection we iterate over that and results of that collection it's right through here it's a bit like a correlated sub-query you in Oracle Oracle you can actually do joins with stuff is not in your current database and it's because that's your team joins with stuff that's not in the working memory this gets more powerful because whereas before we were joining with something that was in the working memory we can have we call global a global essential not type of service variables available here we have a hibernate session this is just globally available and it means that I have zip code in my system I can't put all my people in my in my assistance too big I can put the zip codes in so what I want to do is go through the system zip code by zip code and each zip code I'm going to pull up the people and purchase them Lincoln and that rule just that zip code so it's a hybrid named query sets the parameters set dollar zip code there that's joined from there that same-sex staff and zip code each road that comes back into materialized view it's then going to do a correlated or a nested query against results this hibernate query and we're going to fill to all the people very very simple more powerful see so this is collected collects will collect everything they can find based on the pattern you get so here I st. clicked on my red buses it returns a list you can then evaluate the size of that list we have accumulates EQ let's add you do aggregation this is food pluggable we come out with some average total total setup so we have a number of these these are fully pluggable so FedEx are using this to do geospatial analysis they have a system which you get on my demos where they cool now basically it's it analyzes all of their environments and uses aggregations to work out their vehicles is their rate of climb of the vehicle too much sudden things are very sensitive environment they can't go up too high is a temperature too hot so they use our aggregations to continually summarize the environments and they hook this into other subsystems do those continuous calculations and this is a part of joules fusion which I'll come on to in a minute so this is saying I have all my I get all my bread buses get a reference to takings and put it into this function results this function is to write on you return it it's the number in this case doesn't have your number the number and the number of grid and how to do something it allows me to look at sector data perform calculations on those the this starts to take us into territory of functional programming is actually this from keywords now is taking production all system and the extent of a functional program is allow these conditional industry to be nested and chained so this is quite a complex example to showing how we can combine stubbles into working memory along with stuff that's from a hibernating name query all aggregates by the accumulation using more function Prost programming i won't going for diesels an agency knows their tireless so one of the things off that is you don't want to know just where this is true do this that's not enough because sometimes when it says true do this but i wanted to do it in every hour because it's an alarm or i'm i wanted to do it every month so we interest this content called timers the drawers have a single duration the duration was a safe way of saying if delay the firings it's saying when this is true wait 30 seconds before actually doing it and if it stops being true before guess there's no seconds then stop what you're doing so what we're saying here is when the light is on after a minute 30 seconds turn the light on so it's a timer it will turn the light off after a minute 30 seconds if the light is still we can then have interval based semantics just based on the jdk time util so you have the initial delay and then the iterations after that so this one is just the iteration after that so it's the equivalent of that there wants to have an initial delay before the continual iteration started so this is basically for having a way to make a rule get seriously fine based on delays and iteration periods of course the next thing is a wrapper can do that what can i just combine this with closure to it so now i have my rules that when this rule is true based upon a given Krong definition here so do you like all this stuff based upon the context here is ever know what cron is here it's a conscious way of defining Schilling's and it's incredibly complex very difficult very powerful so we can now include crohn's it means that where this rule is true based on this chrome definition of fire regular intervals we can combine Asperger calendar is not only do much time to fight regulate sports we need to going to save wet if you fire so just good I got the alarm I want to learn to fire every hour it really doesn't need true weekdays so this is cycling as the rule engine on the process engines do that so this is allowing to say that I'm only a week days and this calendars are pretty pluggable based on courts calendars so you give a course calendar to drills no point to find any kind of suck balls pretty impressive based upon weekdays this will fire every app because boot days we've got high priority then I can have an opposite rule that's mutually exclusive it's mutually exclusive to the calendars now weekend's you can have a different priority there so now the info base is four hours so you have two rules of mutual disputes and based upon the kind of the definitions that you can use the standard courts ones we can define your own so now you have timers both instable and chrome based and calendaring all within the rule engine because because every process engine put integrated that means that based upon the calendaring and scheduling you can start processes you can check and then wait States and then unlike the typical systems which are process orientated not only you saying do this in a weekend you're giving a conditional information when to do it as well so it creates a much more path environment much more is everyone's brain hurting I said this would be intense how we doing okay should just make it we're going to lock up a little now I'm going to try and teach a bit more about bra design so ever sing called truth maintenance TMS and in Feroze it variant is a scary word you have companies like corticon going around saying you don't need the furnaces to scare is useless is a tool don't do it and they try and use really complicated examples to show why burn too sunny don't need actually ferret is very resistant but it's very very useful and if you can explain right it becomes quite obvious it's not scary word anymore so let's go through an example about issuing bus passes so children have bus passes and adults have both passes so what we're saying here is that what I'm a person whose ages under 16 created child bus pass for them when have a person who is 16 or older can adult bus pass this couples the logic because imagine I've got a company and the company's splitting - you have one department which chooses the policy who is a child who's not child you have another Department which issues the bus passes the people that issue the bus pass don't necessarily care when that person - 15 or 16 they just told issue bus passes for children issue bus postures for adults so the the decision making is to different departments and what I've done here is that the the Department which issue the bus passes and now tightly coupled decision making and whether it's 16 or over but only is it's happy couple it means they are exposed it is leaking information to the relations not have to care about and it makes the change process complex because it means the department decide the policy to say 16 or 8 or 16 and under they have to issue change requests then go to the other department then have to apply this changer question makes a very brittle process to past and Laura's for your company changing policy decisions what happens with a child stops being 16 as well so it's monolithic because actually it's better in a decision table because it is bringing all your logic into one place it is leaky because it means that one person who's making a decision is seeing information they should not need to care about it is brittle because weapons in a person stops being 16 so how do you get over this so truth makes it has been called a logic insertion a logical assertions of way of saying not only have how to puts one into the working memory not am I going to spray something put it in its life time will depend upon and condition only while that condition is true will exist so only while this person is less than 16 what we have this thing called is child's this child is what call it inference it is a fact that represents the result of a decision it is a fact that represents this person is under 16 that means we have an object which in catalyze some decision-making process about the way it gives us semantic content it's a child and a way that gives it capsulation because you don't know the time you write your patent what makes it we just know this person's a child so it gives an encapsulation because it decoupling and logically syrtis means that this infers that we've made that this person is a child what only exists while the persons under 16 as soon as that person stops being 6 of 16 that fact is automatically deleted removed from the system and then we have a rule that mutually exclusive otherwise get the whip that says when the person's over 16 we logically and sir he is nano so we create an object which encapsulate a decision that crazy parents and them used logic assertion to maintain the truth that I've learnt so this thing that we've inferred this bit of information when we discovered we decide upon we always know it's true based upon some other logic so now that means we have one department that's saying when someone's a child with someone a lot that's their responsibility and they published these rules which creates these appearances we have another department use the spot for four issues bus conscious and now this looks much better they say when a person is a child given a bus pass so he got decoupling we've got encapsulation remove of brittleness we've removed the leakiness much nicer and same here where the person is an adult it should an adult bus pass we can combine this further because when we give the child bus pass we want to have a request to have it back obviously so now we can say when there is no bus pass child bus passes so when there is no longer a child bus pass for this person issue a request so this will happens automatically so you logically insert and child bus pass that child bus pass is based upon the inference we made by the department is less than 16 when a person becomes 16 the inference is automatically retracted because that Affairs automatically attracted we also the child bus is automatic attractive so that means these inferences the chain and truth cascades and if that truth breaks at any point everything blur point is automatically cascaded up so I notice a little bit complicated I hope you just get a bit of it that it's great you've all done very well we're almost there so anyway this is base say when the lodge is cascaded back to the specific points issue a Request it's all done automatically by the system so truth maintenance and ferns gives you decoupling of knowledge responsibilities in cap states phrenology provides semantic abstractions whereas encapsulation because in a person is raised to 16 less than 16 what does that mean I don't know what that means it's just something it means different things to different people you know if it was age in a sense that's different in different countries so it allows you to encapsulants give it semantic meanings it's quite important mixed things read what makes them more maintainable and the truth made sense as well helps add integrity robustness so I think that's just a little smattering of a rule-based theory ok this is last bit is about our transfusion two drills fusion extends our existing rule language with a vet processing capabilities or complex event processing capabilities there are number of different engines that have evolved for this called query based engines so query based engine typically based on SQL and they just extreme of data and they look for changes that stream of data and they use and design practical event condition action basically means they're going to submit an event based once like they found for me this is actually quite limited and I do another talk if I had more time but I start to show comparison controls and Esper because I only read us around two different languages to do the same thing one language is a very subset the other these query based systems do not support side effects I weapons have information your query changes all engines do they don't have all the powerful features that we really like true coincidence and already that get to learn two different API you have to learn to ways of building it means that you got to learn everything two or three times to be got if you were to use a process engine rule engine and invent percent you want error you have to learn the API for each you have to learn the language breach you're going to have to learn the nuances of each one and as you know you can learn one thing quite efficiently when you go to the next one you may be the same at what you get less efficient I think it's a thermal you're less efficient again so you guys spend a week learning how to read how to build something how to deploy something how to debug something how to check errors all these three different systems and as you get older you get some in here in your 20s you don't mind doing it when you go to 30s you get sick and tired you're like I don't want to deal with this I just want to do my job it's not interesting it's not fun anymore it's like there when I was 24 we can store a different Linux distribution anyway now I hate this about two windows oh I love red I don't use windows I am an economist this isn't this is not a Windows desktop Hey so anyway haven't much time drools uses a full rule-based approach to complex event processing TIBCO do as well many of the members who are query based have more limited systems put a lot of files so why you need this special system need a special system can do this cover like Apple are Esper many of them try to put down roll engines it's all just bollocks it's simply because the existing world engines have not been designed yet to do that does not mean they can't do it so if you said yes you started trying to combat to impress on Jess of course it's going to blow is only it's not just can't do this it just has to be extended yet forth and there's no reason why these systems can't be extended so we've taken many of the things that needed to address combat spent processing made it possible a rule engine okay so first of all the rule engine has a single point of its social which creates a bottleneck so if you've got ten different queues of streams of data coming in what you don't want to do is to have to keep having more going through one point of fun and the other problem they'll say is that if you've got to work your memory it sees everything and if everything has to be evaluated to be quite slow and cumbersome so you're creating these things called entry points an entry point into way to partition a knowledgebase and these are names typically for each stream of data you have will become an entry points so here we have the whole burger stream that when you catch up to Jamis although it's HTTP whatever you want and I have a little reduce consumer which effectively into this named entry point so that's on the API side on the language side we can now have a pattern frog and entry points so what it means that in this pattern here doesn't filter everything's been working and really filter sucked on this stream learning that because serve range rules worked naturally these streams I've seen these our entry points can be correlated with other entry points and we try and make sure that each entry point doesn't match its own threads possible so what that means is each entry points automatically on its own thread and we will take care of the synchronization of the join of the two threads we try and do as much in its own threads getting the efficiency of throughput so that takes care of that rule engines do not have temporal comparison spray difference to comparisons of time draws is next only with all thirteen temporal operators that done something they can equal a gin model any type of time and you can do this by saying this comparator and don't compares have different arguments because you can see when so after this gets off this between 1 seconds and 10 seconds so this is saying when I have a buyer acknowledgment event there's between 171 seconds and 10 seconds after a buy order events how can understand visually a before be a me to be able a to be a pinches be shows the visual context of that whisp or 13 of those just means you the basically you express everything at the time it's not just important to know where time happens when something happens it's often more important I would say does not happen so what does that mean if I have a by order of it what I want to know which is really important is when the buyers moment does not happen so what I'm saying here is when I have a buy order event when it does not happen between 1 and 10 seconds and by a by remote event that I need to do something and the notice again we now have effective three different our areas with a working memory and my magic default entry point have my home breaker stream I have my stock trader stream it's correlated all three of these check absences events how much code do you have to write to do this in our HQ hello and you have to maintain it I showed this to go to bank they banks all of that time everything do is about time and they write this complex systems to analyze the change of the stopped at time aggregation in time and they have to try and test us when you have theta and rules that are working the time is incredibly brutal incredibly British very hard when you take someone who to spend day and day out dealing with websites enema timers and synchronizations and all the low-level code to try and make this example and you show them this they fall instantly love so Georgia banking is like a revelation when they saw this but it gets even better so we can take these patterns and we can create Windows we can say when this pattern happens over a period of time so we can say when this pattern happen it happens every time window for five seconds or we can get the counts when the stock ticker happens over left of a thousand tickets because what we do is orthogonal you learn more languages learn a few key words to extend the rule language prevent processing capabilities so that means you already let accumulate you've already learned from running up where your patent which we learn we rules you don't have to go away I've learned a whole new thing for the CP it's just an over keyword the 13 operators you've already learned and I said now you've got four super capabilities your existing knowledge so allow you to do more by learning less think about it do more learning less that's great this is going produced the average stock price over time within five seconds the average stock price of time within five seconds it's more than hundred than do something as I said teach these functions are fully pluggable is a a jar interface you register that and you can do anything you want that Emily will work with any subsystem you like that does statistical analysis to try and show you a little bit how this works with processes we very much when you start to have something that has built in event currencies you want to start designing for vendor in architecture what does that mean it means you want to make sure that everything in your system emits an event do everything with events everything that happens whether someone needs to consume I'm not doing events every state change a mitten event so whether the rule engine every time we're working every time we insert something in from the start process and time the little fire to come in today if this was a business application you can catch the events too you know someone who's fire someone is hired a time you buy a stock all these are events and you if you can model everything that happens every state change that happened in your system you just omit these allow you to create systems which are far less brittle large credit systems which allows you to do things within which you might not necessarily attended so you design system in a good event model upfront and then use trying to build it with correlation so here do you think about process what is the process the process is just correlation as sequential correlation of events as it goes from know to know to know to know it is just an event and it has a state change and environment so you can admit events for needs it means the complex event processing side can supple this in and you can analyze it so just very very simple one here I have a process started events so have an order process and it's stating that on every time an order process starts this event will be mixed automatically by the system and I think over a time window one hour do account aggregation if over a period of an hour this process is started more than a thousand times then do something one of the thickness dead where systems are going and pretty much at the own ass five minutes necessary assistant are moving more to call dynamic and adaptive so dynamic means the systems can change on the fly that means rule edges have always been done if you add rules you can movement and a stateful system a stateful system while running you don't have to take it down you don't have to put it back up again there's a saver processes in our system and rules you can add processes debris processes remember the knowledge bases it's a composite knowledge you can change any part of these not only that our process is related to change sub parts of that we call this dynamic fragments orchestration so this is about the done outside now if you can have sight this monitoring itself you get scented active because it can monitor what's doing and then it can change itself start gets a most dynamic and adaptive system to the future will be that it will be both clicks and binding and they will be adapted that we sell military questions I gave a supposed to be patience it never has a headaches like very very sorry and since I have a lot to go through and so there's so much more it takes it takes about three to four hours to do this justice I hope you understand small amounts of it and I hope people learn something today questions you you