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1 Formula Status Update New Usage Patterns for FINREP Herm Fischer Formula Working Group 2008-04-20.

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Presentation on theme: "1 Formula Status Update New Usage Patterns for FINREP Herm Fischer Formula Working Group 2008-04-20."— Presentation transcript:

1 1 Formula Status Update New Usage Patterns for FINREP Herm Fischer Formula Working Group 2008-04-20

2 2 Formula Status Status = Proposed Recommendation – 45 day wait before Recommendation Four known implementations –Fujitsu & UBmatrix conformant, in production use –CompSci Resources & New Lido implementations Major stake holders –BdE, BdF, BoJ, SEC deployed –FDIC has early-IWD formulas

3 3 Specifications are Extendable Basic usage patterns in PR spec –Producing fact items (output instance document) –Assertions for Consistency (produced fact vs. reported fact) Existence Value –Single input, single output of instances Extension usage patterns

4 4 Implementation prototypes Message composition Formula chaining Tuple generation Multi-instance processing Linkbase & footnotes functions Custom functions implemented in XPath Assertion sets Interesting, not implemented yet Very Large Instances processing

5 5 Existing prototypes Chaining and tuple generation –XSB required prototype to assure PR supports it Multi-instance processing –Became core part of chaining proposal –Prototyped (partially) Linkbase tree walks –Prototype allows linkbase to control formulas of Calc linkbase & dimension aggregation roll ups Movements & totalling by presentation linkbase

6 6 Discussing by history vs. Discussing by FINREP urgency Message generation (needed) Chaining (cool, but getting by without this) Tuple generation (just for GL people?) Multi-instance (it is the chaining solution) Custom functions with XPath (need this) Linkbase functions (top priority) –Patterns in linkbase eliminate most formulas –Moves formula semantics from code to linkbase –Critical for success Assertion sets (in use)

7 7 Chaining Most frequently request usage case Least needed (based on experience) Two solutions implemented –Multi-instance approach most flexible –Explicity dependencies useful for tuple output Facilitates modularization Helpful to manage large formula projects

8 8 Chaining with explicit dependency Prior result passed as factVariable

9 9 Dependency is explicit The arc assigns prior result to a factVariable The \author explicitly specifies this dependency Important for tuple output Difficult to maintain in large formula sets Difficult to factor formulae to separate files

10 10 Tuple chaining must be explicit

11 11 Muti-instance chaining solution Common solution to multi-instance and chaining Lets first look at multi-instance and then come back to chaining that uses it

12 12 Other multi-instance solutions Complete DTS with linkbases needed –For tree walks: movements, totaling, and dimensions fn:doc() not useful to load instances –Does not discover DTS –Does not load linkbases –Can not handle shared DTS components Some referenced taxonomies common Some linkbases evolve

13 13 Prior: xfi:inst() was prototyped Provides a fn:doc() counterpart to load DTS generalVariables can access these instances –No filtering on multi-instances –factVar, filter on primary instance, genVar, XPath on additional instances Requires formula execution code to load instances (instead of formula processor infrastructure)

14 14 New multi-instance features All instances loaded by infrastructure (doesnt have to be coded) Filters, functions, sequence, covering work Should share formulas & filters - all instances

15 15 New multi-instance solution Instances are represented by instance resource instance-variable arc to factVariable –If present, specifies non-default source instance formula-instance arc from formula –If present specifies the instance to receive fact Instance resources are files or temporary

16 16 Instance resources Could be loaded by processor –E.g., java code in a server loads primary instance and some prior-period or other-company instances –Or user of GUI adds additional instances, such as loading prior-period or other-company instances Default implied source and result instances Can be temporary in memory only –Used for chaining and modularization

17 17 Multi-instance solution A better approach to chaining Implements multiple instance documents Applies to very large instance solution

18 18 Multi-source and result instances

19 19 Aspect sources, implicit filtering Formula aspects come from its variables Variables from different instances contribute aspects –Aspects independent of the instances they come from –Aspect covering is by-aspect, not by-instance

20 20 A=B+C; C=D+E use case (Explicit dependency chaining) Formula 1 (C=D+E) –Result is C, factVariables D & E –factVariables D & E are from the source instance Formula 2 (A=B+C) –Arc from formula 1, name $r given to Formula 1 result –Result is A, factVariables B & C –factVariable B is from source instance –factVariable $r is from result of formula 1

21 21 A=B+C; C=D+E (Example 0027 v-01) Explicit dependency chaining

22 22 A=B+C; C=D+E use case (Multi-instance chaining) Formula 1 (A=B+C) –Result is A, factVariables B & C –factVariable B is from source instance (default) –factVariable C is from result instance (has an arc) Formula 2 (C=D+E) –Result is C, factVariables D & E –factVariables D & E are from the source instance

23 23 A=B+C; C=D+E (Example 0026 v-01) Multi-instance chaining

24 24 COREP Use case 18: Weighted average of member children Weighted average of its dimensional children by another primary item

25 25 Current single-formula solution Excel formulas: Make PD controlling fact, get PD and EV of dimensional children General variable for PDxEV member matching

26 26 Single formula (Example 0017 v-01) difficult to explain

27 27 Exposure value formula Each PD x EV produced by one formula –Result factItem PDxEV is the product for each dimension value Second formula binds PDxEVs of dim-children to sequence and EVs of dim-children to second sequence, value assertion checks result

28 28 New idea: multiple result instances The PDxEV result fact items arent needed for a real result instance Only a value assertion is really needed A temporary-results instance might be useful Also a temporary facts DTS would be needed (to define the PDxEV result fact item)

29 29 Chained formulas (0026 v-20)

30 30 Implementation issues Multi-instance term binding –Variables can be bound to different source instances –(This already exists in xfi:inst() based solution.) –Each term in XPath knows its instance/DTS (in the internal model or DOM of implementation) Function binding –A function with item results must keep the instance/DTS of the function result (based on the input terms)

31 31 Tree walking Current implementation –Navigation returns concepts and attributes ( (c1, c2, c3), (a11, a12, a13), (a21, a22, a23) ) –take subsequences with XPath for-loops –working (geeky) Change idea (not prototyped yet) –Navigation returns fully resolved relationship nodes A relationship has reference to arc node attributes –Attributes: rel/@weight, rel/@preferredLabel –Concepts: maybe xfi:from/xfi:to( rel-node )

32 32 Use of tree walking Calculation linkbase checking by formula –Uses xfi function for linkbase tree walk –Roll ups compared By threshold value By rounded values same as ordinary calc validation –Extended links managed by formula EDInet consolidated vs nonConsolidated conflicts Dimension aggregation by formula –Uses dimension filter child/descendant feature

33 33 FINREP formulas Most current formulas can be custom tree walk –Consider optional/required attribute –Consider fall back values by arc attribute –Consider dimension filter by arc attribute –Other attributes as needed Replace 72± (BdF count) with few tree walks

34 34 Very Large Instances Use Case Sizes > ½ million facts, > ½ GB DOMs –Census, Tax office, Security exchanges, etc. Multi-GB heaps not feasible with Java VMs –moribund at couple GB (incl code & data) Data almost always from Relational DBMSes

35 35 Very Large Instances approach Basic PR formula solution –All facts, all filters, all variable sets in parallel –Not feasible with very large single- or multi-instances Multi-instance approach –Allows modularizing processing –Stage formulas to work on parts of very large instance –Cooperative filters & (stored) SQL DB interfaces –Intermediate result instances pass between stages

36 36 Staged multi-instance strategy SQL Very Large Relational DB Formula Linkbase(s) lazy load early GC instance filters formula & variables interim instance filters formula & variables filters formula & variables result facts interim instance

37 37 Custom functions with XPath Custom functions in PR require Java code Examples of custom functions –Taxonomy and linkbase access –Math with exponentials and recursion (loan value calc) Prototype adds XPath implementation

38 38 a(b,c) = $b + $c (Example 0030 v01)

39 39 Precision by unit (Example 0030 v-03) my-fn:decimals($b) value=" for $unit in local-name-from-QName( xfi:measure-name( xfi:unit-numerator( xfi:unit( $item ))[1] )) return ( if ($unit eq 'JPY') then -5 else -2 ) " >

40 40 Recursion (Example 0030 v-04) <function-impl:function xlink:type="resource" xlink:label="cust-fn-a" name="my-fn:power" output="xs:decimal" value=" if ($exp lt 0) then ( 1 div my-fn:power($y, - $exp) ) else ( if ($exp lt 1) then 1 else ($y * my-fn:power($y,$exp - 1)) ) " >

41 41 Present value (Example 0030 v-05) <formula:formula xlink:type="resource" xlink:label="formula1" value="$amountDue * my-fn:power((1 + $interestRate), $numYears)"

42 42 Back to FINREP Segue to –Use of tree walks to consolidate many formula

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