Making Sense of Medicare Data: From Mining to Analytics
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The document outlines the transition from traditional Medicare claims processing to advanced data analytics using HPCC systems, detailing Medicare's structure, bundled payment arrangements, and data processing methods. It highlights the capabilities of the HPCC platform for big data management, integration, and analysis with a focus on declarative programming using ECL, which allows for efficient data operations. Additionally, it discusses beyond-processing considerations such as security, collaboration, unit testing, and visualization for coding and data delivery within the health care analytics framework.
![Processing Claims
1. The intent here is to make the series of interim claims look like a single claim for
most purposes, where the admission date of the first claim becomes the
admission date of the whole claim and the discharge date of the last claim in the
series becomes the discharge date of the whole claim.
2. The admission date from the first series in the claim and the discharge date
from the last series in the claim define the length of the stay.
3. The MS-DRG from the last claim in the single stay (the discharge MS-DRG)
determines whether the hospital stay becomes an anchor record, or whether the
stay is included/excluded as a readmission for an existing episode.
4. Costs across all IP claims included in the single stay are aggregated to the stay
level.
5. Claims where the last in the series of claims has patient (…) [as “still a patient”,
not discharged], flag these and drop all of the claims in the series from the IP
hospital stay file.
23](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-23-320.jpg)
![Processing Claims With ECL
H_1 := SORT( A , bene_sk, provider, admsn_dt, dschrgdt, thru_dt);
H_2 := ROLLUP(H_1,
is_interim(LEFT, RIGHT),
merge_interim_claims(LEFT, RIGHT));
H_3 := JOIN(H_2, H_1, LEFT.bene_sk = RIGHT.bene_sk […], RIGHT
ONLY);
H_4 := PROJECT(H_3, TRANSFORM(BPCI.Layouts.ip_claim_etl_layout,
SELF.is_dropped := TRUE;
SELF.dropped_reason_code :=
BPCI.Layouts.DROPPED_REASON_CODES.InterimClaim;
SELF := LEFT;
));
H := H_2 + H_4;
24](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-24-320.jpg)
![25
Template Language
EXPORT load_all_client_files(pId, pFileSet, pBaseDataDirectory) := MACRO
LOADXML(pFileSet);
baseDataDirectory := pBaseDataDirectory + pId + '/';
#FOR(folder)
#UNIQUENAME(subId)
%subId% := %''%;
#UNIQUENAME(subDS)
%subDS% := Client.Datasets(%subId%);
[...]
#UNIQUENAME(id)
%id% := pId + '::' + %''%;
#UNIQUENAME(dataDir)
%dataDir% := %baseDataDirectory% + %''% + '/';
#UNIQUENAME(etl)
%etl% := Client.ETL(%dataDir%, %id%);
%etl%.run();
#END
ENDMACRO;](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-25-320.jpg)
![34
Beyond Processing: Unit Tests
interim_claims := MODULE
// Test Data
test_set :=
BPCI.Test.Samples.ip_claim(
bene_id := 1, claim_id := 1, pmt_amt := 3042.0, ...)
+ BPCI.Test.Samples.ip_claim(
bene_id := 1, claim_id := 2, pmt_amt := 11409.0, ...)
+ ....
;
...
EXPORT Actual := Step2.ip_stays;
SHARED TestSuite := MODULE
EXPORT Test01 := ASSERT(oActual(NOT is_dropped), claimno IN [1,2], 'Did not filter ou
EXPORT Test02 := ASSERT(oActual(is_dropped), claimno IN [3,5]);
END;
EXPORT AllTests := TestSuite.Test01 + TestSuite.Test02;
END;](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-34-320.jpg)
![Beyond Processing: Unit Tests (2)
35
// Using inline dataset
simple_ip_claims := DATASET([
{1,1,'0','010001',20000201,'',20000120,200
00201,'61'},
], simplified_ip_layout);
ip_claims :=
Samples.ip_claims(simple_ip_claims);
// OR passing NAMED parameters
ip_claims := Samples.ip_claims2(
bene_id := 1,
claim_id := 1,
claim_type := '00'
)
simplified_ip_layout := RECORD
UNSIGNED bene_id;
UNSIGNED claim_id;
STRING claim_type;
STRING provider_number;
INTEGER4 through_date;
STRING status_code;
INTEGER4 admission_date;
INTEGER4 discharge_date;
STRING ms_drg_code;
END;](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-35-320.jpg)
![Data Service: Query
• Define service using ECL
42
INTEGER4 oOffset := 1 : STORED('Offset');
INTEGER4 oResults := 100 : STORED('Results');
INTEGER4 oStartDate := 20130201 : STORED('Begin_Date');
INTEGER4 oEndDate := 20140201 : STORED('End_Date');
...
oParams := DATASET([{ oOffset, oResults, oStartDate, oEndDate, … }],
Layouts.service_parameters_layout);
T(DATASET(RECORDOF(Datasets.dsFactEpisodeCostsIndex)) pData) := FUNCTION
RETURN TABLE(pData,
{
STRING bpid := pData.bpid;
UNSIGNED INTEGER1 model := pData.model;
UNSIGNED INTEGER1 post_dsch_prd_length := pData.post_dsch_prd_length;
INTEGER8 total_episodes := COUNT(GROUP);
DECIMAL15_2 total_costs := SUM(GROUP, sum_post_dsch_prd_pay);
DECIMAL15_2 average_costs := AVE(GROUP, sum_post_dsch_prd_pay);
DECIMAL15_2 std_dev_costs := SQRT(VARIANCE(GROUP, sum_post_dsch_prd_pay));
}, bpid, model, post_dsch_prd_length);
END;
ReportServices.BaseService.run_it('Summary', oParameters, T, bpid);](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-42-320.jpg)
![43
Data (Web) Services
{
"summary": {
"offset": 1,
"results": 10,
"begin_date": 20130101,
"end_date": 20130201,
…
}
}
{
"summaryResponse": {
…
"Results": {
…
"Summary": {
"Row": [
{
"bpid": "9999",
"model": 2,
"post_dsch_prd_length": 90,
"total_episodes": 987,
"total_costs": 9876543.21,
"average_costs": 12358.13
…
}
]
…
Request Response
https://.../WsEcl/forms/json/query/roxie/summary](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-43-320.jpg)
![48
Data Services: Reusability
EXPORT run_it( pServiceName, pParams, pReportFunction, pSortByField) := MACRO
// Filtering data based on parameters
#UNIQUENAME(DS);
%DS% := WS.Datasets.dsFactEpisodeCostsIndex;
[…]
#UNIQUENAME(B)
%B% := IF(COUNT(pParams[1].providers) = 0, %A%, %A%(provider_id IN pParams[1].providers));
#UNIQUENAME(C)
%C% := IF(COUNT(pParams[1].npis) = 0, %B%, %B%(at_npi IN pParams[1].npis OR op_npi IN pParams[
[…]
#UNIQUENAME(report)
%report% := pReportFunction(%K%);
#UNIQUENAME(sorted)
%sorted% := SORT(%report%, pSortByField);
#UNIQUENAME(O1)
%O1% := OUTPUT(pParameters, NAMED('Request'));
oSummary := DATASET([{ COUNT(%sorted%) }], WS.Layouts.service_summary_layout);
#UNIQUENAME(O2)
%O2% := OUTPUT(oSummary, NAMED(‘Metadata'));
#UNIQUENAME(O3)
%O3% := OUTPUT(
CHOOSEN(%sorted%, pParams[1].results, pParams[1].offset),
NAMED(pServiceName), ALL);
PARALLEL(%O1%, %O2%, %O3%);
ENDMACRO;](https://image.slidesharecdn.com/hpccdbmonth-150518150905-lva1-app6892/85/Making-Sense-of-Medicare-Data-From-Mining-to-Analytics-48-320.jpg)
Making Sense of Medicare Data: From Mining to Analytics
- 1. 1 From Mining To Analytics Making Sense of Medicare Data
- 5. Medicare • Centers for Medicare & Medicaid Services (CMS) • Medicare is a national social insurance program since 1966 covering Americans aged 65 and older. • “Fee For Service” Model 5
- 6. Medicare: Fee for Service 6 Hospital SNF CMS ClaimClaimClaimClaims ClaimClaimClaimClaims ClaimClaimClaimClaims ClaimClaimClaimClaims Home HealthHospital (Readmit) 3 days 18 days 2 days 12 visits $3,000 $12,000 $5,000 $4,000 = $24,000
- 7. Bundled Payments for Care • Better care, smarter spending, and healthier people • The Bundled Payments for Care Improvement (BPCI) Payment arrangements Based on financial and performance • 4 Models: Model 1: Retrospective Acute Care Hospital Stay Only Model 2: Retrospective Acute Care Hospital Stay And Post- acute care Model 3: Retrospective Post-Acute Care Only Model 4: Acute Care Hospital Stay Only 7
- 8. Medicare: BPCI 8 Hospital SNF CMS ClaimClaimClaimClaims ClaimClaimClaimClaims Home HealthHospital (Readmit) 3 days 18 days Episode $20,000 $20,000 - ($3,000 + $12,000) = $5,000 $3,000 $12,000
- 9. Claims • Statement of services and costs from a healthcare provider Patient information Diagnosis information Procedure(s) information • Types: Hospital (Inpatient) Claims Skilled Nursing Facility (SNF) Claims Home Health Agency (HHA) Claims … 9
- 10. 10 Episode of Care SNF #1 SNF #2 IP #1 IP #2 SNF #2 IP #3 HHA #1 HHA #2 time
- 11. Mining Claims 11 IP SNF HHAIP IP IP IP SNF SNF IP SNF HHAIP IP IP IP SNF SNF IP SNF HHAIP IP IP IP SNF SNF IP SNF HHAIP IP IP IP SNF SNF IP SNF HHAIP IP IP IP SNF SNF IP IP IP IP IP Hospital Stays Post-Acute Care Anchor Episode Initiator
- 12. Technologies considered • Pig (& Hadoop) Data Processing Language Procedural Language Relational-oriented • SAS Business Analytics & BI Software De-facto standard in Healthcare Industry Proprietary 12
- 13. HPCC Systems Quick Introduction Rodrigo Pastrana -Consulting Software Engineer
- 14. WHT/082311 What is HPCC Systems 14 • Open Source distributed data-intensive computing platform • Provides end-to-end Big Data workflow management , scheduler, integration tools, etc • Runs on commodity computing/storage nodes • Binary packages available for the most common Linux distributions • Originally designed circa 1999 (predates the original paper on MapReduce from Dec. ‘04) • Improved over a decade of real-world Big Data analytics • In use across critical production environments throughout LexisNexis for more than 10 years
- 15. WHT/082311 The HPCC Systems platform 15
- 16. WHT/082311 • Massively Parallel data processing engine • Enables data integration on a scale not previously available • Programmable using ECL HPCC Systems Data Refinery (Thor) HPCC Systems Data Delivery Engine (Roxie) • A massively parallel, high throughput, query engine • Low latency, highly concurrent and highly available • Several advanced strategies for efficient retrieval • Programmable using ECL Enterprise Control Language (ECL) • An easy to use, declarative data-centric programming language optimized for large-scale data management and query processing • Highly efficient; automatically distributes workload across all nodes; compiles to native machine code. • Automatic parallelization and synchronization 1 2 3 The Three HPCC Systems components Conclusion: End to End platform • No need for any third party tools 16
- 17. WHT/082311 • Declarative programming language: Describe what needs to be done and not how to do it • Powerful: High level data activities like JOIN, TRANSFORM, PROJECT, SORT, DISTRIBUTE, MAP, etc. are available. • Extensible: Modular and extensible, it can shape itself to adapt to the type of problem at hand • Implicitly parallel: Parallelism is built into the underlying platform. The programmer needs not be concerned with data partitioning and parallelism • Maintainable: High level programming language, without side effects and with efficient encapsulation; programs are more succinct, reliable and easier to troubleshoot • Complete: ECL provides a complete data programming paradigm • Homogeneous: One language to express data algorithms across the entire HPCC Systems platform: data integration, analytics and high speed delivery • Polyglottic: ECL supports the embedding of other languages such as Java, Python, R, SQL, and more Enterprise Control Language (ECL) 17
- 18. WHT/082311 Current Status and Resources • HPCCSystems.com – Tutorials, Docs, Platform distributions, and more • Latest release 5.2.0 adds many new features and improvements • Drastic GUI improvements • Ganglia and Nagios plug-in for system monitoring and alerting • Security Enhancements – tighter authentication measures, intra- component communication encryption • Embedded Languages – Cassandra support, memcache and redis access • JSON based data support • Dynamic ESDL – Provides simple middleware/back-end interface definition • JAVA API project – facilitates interaction between Java based apps and HPCC web services and c++ tools • Available now – HPCCSystems.com
- 19. Data mining with HPCC Systems • Thor Responsible for processing vast amount of data Optimized for Extraction, Transformation, Loading, Sorting and Linking Data • ECL Declarative More Data Centric Fast & Implicitly Parallel Inline data Unit Tests in ECL 19
- 20. 20 SQL vs ECL SELECT diag_group_cd, COUNT(*) as volume SUM(pmt_amt) as costs FROM inpatient_claims GROUP BY diag_group_cd; TABLE( inpatient_claims, { diag_group_cd; INTEGER volume := COUNT(GROUP); REAL costs := SUM(pmt_amt); }, diag_group_cd ); SQL ECL SELECT * FROM inpatient_claims LEFT JOIN ip_value_codes RIGHT ON LEFT.id = RIGHT.id JOIN( inpatient_claims, ip_value_codes, LEFT.id = RIGHT.id );
- 21. 21 SQL vs ECL DECLARE my_cursor CURSOR FOR SELECT * FROM inpatient_claims; OPEN my_cursor FETCH NEXT FROM my_cursor INTO @…, @… WHILE @@FETCH_STATUS = 0 BEGIN … END CLOSE my_cursor; DEALLOCATE my_cursor; ITERATE( inpatient_claims, TRANSFORM(inpatient_claim_layout, SELF.is_dropped := is_one_year_or_greater( RIGHT.admsn_dt, RIGHT.dschrgdt); SELF := RIGHT; ) ); SQL ECL
- 22. Tx 22 ECL ROLLUP R1 R2 R3 R4 R5 R6 LEFT RIGHT RA TxLEFT RIGHT RB R4 R6R5 ROLLUP( dataset, condition(LEFT, RIGHT), transformation(LEFT, RIGHT) )
- 23. Processing Claims 1. The intent here is to make the series of interim claims look like a single claim for most purposes, where the admission date of the first claim becomes the admission date of the whole claim and the discharge date of the last claim in the series becomes the discharge date of the whole claim. 2. The admission date from the first series in the claim and the discharge date from the last series in the claim define the length of the stay. 3. The MS-DRG from the last claim in the single stay (the discharge MS-DRG) determines whether the hospital stay becomes an anchor record, or whether the stay is included/excluded as a readmission for an existing episode. 4. Costs across all IP claims included in the single stay are aggregated to the stay level. 5. Claims where the last in the series of claims has patient (…) [as “still a patient”, not discharged], flag these and drop all of the claims in the series from the IP hospital stay file. 23
- 24. Processing Claims With ECL H_1 := SORT( A , bene_sk, provider, admsn_dt, dschrgdt, thru_dt); H_2 := ROLLUP(H_1, is_interim(LEFT, RIGHT), merge_interim_claims(LEFT, RIGHT)); H_3 := JOIN(H_2, H_1, LEFT.bene_sk = RIGHT.bene_sk […], RIGHT ONLY); H_4 := PROJECT(H_3, TRANSFORM(BPCI.Layouts.ip_claim_etl_layout, SELF.is_dropped := TRUE; SELF.dropped_reason_code := BPCI.Layouts.DROPPED_REASON_CODES.InterimClaim; SELF := LEFT; )); H := H_2 + H_4; 24
- 25. 25 Template Language EXPORT load_all_client_files(pId, pFileSet, pBaseDataDirectory) := MACRO LOADXML(pFileSet); baseDataDirectory := pBaseDataDirectory + pId + '/'; #FOR(folder) #UNIQUENAME(subId) %subId% := %''%; #UNIQUENAME(subDS) %subDS% := Client.Datasets(%subId%); [...] #UNIQUENAME(id) %id% := pId + '::' + %''%; #UNIQUENAME(dataDir) %dataDir% := %baseDataDirectory% + %''% + '/'; #UNIQUENAME(etl) %etl% := Client.ETL(%dataDir%, %id%); %etl%.run(); #END ENDMACRO;
- 26. 26 Template Language file_set := ’<folders>’ + '<folder>M201409</folder>' + '<folder>M201410</folder>' + '<folder>M201411</folder>' + '<folder>M201412</folder>' + '<folder>M201501</folder>' + '<folder>M201502</folder>' + '<folder>M201503</folder>' + ‘</folders>’; load_all_client_files(1234, file_set, ‘/volume1/data/‘);
- 27. Beyond Processing Data • Security & Authentication • Collaboration • Unit Tests • Visualizations 27
- 28. Beyond Processing: Security • HTTPS • Htpasswd • LDAP support • File level security when using LDAP 28
- 29. Beyond Processing: Workunits • Workunit Identifier • Attribution • Query • Timings • Results 29
- 34. 34 Beyond Processing: Unit Tests interim_claims := MODULE // Test Data test_set := BPCI.Test.Samples.ip_claim( bene_id := 1, claim_id := 1, pmt_amt := 3042.0, ...) + BPCI.Test.Samples.ip_claim( bene_id := 1, claim_id := 2, pmt_amt := 11409.0, ...) + .... ; ... EXPORT Actual := Step2.ip_stays; SHARED TestSuite := MODULE EXPORT Test01 := ASSERT(oActual(NOT is_dropped), claimno IN [1,2], 'Did not filter ou EXPORT Test02 := ASSERT(oActual(is_dropped), claimno IN [3,5]); END; EXPORT AllTests := TestSuite.Test01 + TestSuite.Test02; END;
- 35. Beyond Processing: Unit Tests (2) 35 // Using inline dataset simple_ip_claims := DATASET([ {1,1,'0','010001',20000201,'',20000120,200 00201,'61'}, ], simplified_ip_layout); ip_claims := Samples.ip_claims(simple_ip_claims); // OR passing NAMED parameters ip_claims := Samples.ip_claims2( bene_id := 1, claim_id := 1, claim_type := '00' ) simplified_ip_layout := RECORD UNSIGNED bene_id; UNSIGNED claim_id; STRING claim_type; STRING provider_number; INTEGER4 through_date; STRING status_code; INTEGER4 admission_date; INTEGER4 discharge_date; STRING ms_drg_code; END;
- 38. 38 (No) Insights 0 20 40 60 80 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 Episodes Costs in $1,000
- 39. 39 Insights 0 20 40 60 80 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 Episodes Costs in $1,000 No readmit 1 Readmit 2 Readmits
- 40. Data Delivery: Roxie • Data Delivery Engine • Indexed, compressed and in-memory • Data Warehouse Capabilities • Data Services 40
- 41. Data Services • Web Services over Data Warehouse • XML/SOAP but also JSON • Web Services defined in ECL • Solution to add/remove data from the cluster 41
- 42. Data Service: Query • Define service using ECL 42 INTEGER4 oOffset := 1 : STORED('Offset'); INTEGER4 oResults := 100 : STORED('Results'); INTEGER4 oStartDate := 20130201 : STORED('Begin_Date'); INTEGER4 oEndDate := 20140201 : STORED('End_Date'); ... oParams := DATASET([{ oOffset, oResults, oStartDate, oEndDate, … }], Layouts.service_parameters_layout); T(DATASET(RECORDOF(Datasets.dsFactEpisodeCostsIndex)) pData) := FUNCTION RETURN TABLE(pData, { STRING bpid := pData.bpid; UNSIGNED INTEGER1 model := pData.model; UNSIGNED INTEGER1 post_dsch_prd_length := pData.post_dsch_prd_length; INTEGER8 total_episodes := COUNT(GROUP); DECIMAL15_2 total_costs := SUM(GROUP, sum_post_dsch_prd_pay); DECIMAL15_2 average_costs := AVE(GROUP, sum_post_dsch_prd_pay); DECIMAL15_2 std_dev_costs := SQRT(VARIANCE(GROUP, sum_post_dsch_prd_pay)); }, bpid, model, post_dsch_prd_length); END; ReportServices.BaseService.run_it('Summary', oParameters, T, bpid);
- 43. 43 Data (Web) Services { "summary": { "offset": 1, "results": 10, "begin_date": 20130101, "end_date": 20130201, … } } { "summaryResponse": { … "Results": { … "Summary": { "Row": [ { "bpid": "9999", "model": 2, "post_dsch_prd_length": 90, "total_episodes": 987, "total_costs": 9876543.21, "average_costs": 12358.13 … } ] … Request Response https://.../WsEcl/forms/json/query/roxie/summary
- 46. Loading up data • Logical vs Physical ~abc::subfolder::subsubfolder::myfile /abc/subfolder/subsubfolder/myfile • ECL to load data into cluster: 46 oDS := DATASET( std.File.ExternalLogicalFilename('172.0.0.1','/var/lib/.../myfile.csv'), Layouts.ip_claim_layout, CSV(HEADING(0)) ); oDSDistributed := DISTRIBUTE(oDS, bene_id); OUTPUT(oDSDistributed,, ‘~somewhere::over::here::myfile’, OVERWRITE); oDS := DATASET(‘~somewhere::over::here::myfile’, Layouts.ip_claim_layout); • ECL to use data loaded into cluster:
- 47. SuperFiles • Super File = Symbolic link, list of sub-files • Each sub-file must have the same layout 47 WEBLOGS_FILE := ‘~somewhere::logs::web’ Std.File.CreateSuperFile(WEBLOGS_FILE); … run_report() := FUNCTION oDS := DATASET(WEBLOGS_FILE, Layouts.weblogs_layout, CSV); RETURN TABLE(oDS, { ip_address; COUNT(GROUP); }, ip_address ); END; SEQUENTIAL( Std.File.StartSuperFileTransaction(), Std.File.AddSuperFile(WEBLOGS_FILE, ‘~somewhere::logs::web::2015::04::01’), Std.File.FinishSuperFileTransaction() ); • Including (more) data:
- 48. 48 Data Services: Reusability EXPORT run_it( pServiceName, pParams, pReportFunction, pSortByField) := MACRO // Filtering data based on parameters #UNIQUENAME(DS); %DS% := WS.Datasets.dsFactEpisodeCostsIndex; […] #UNIQUENAME(B) %B% := IF(COUNT(pParams[1].providers) = 0, %A%, %A%(provider_id IN pParams[1].providers)); #UNIQUENAME(C) %C% := IF(COUNT(pParams[1].npis) = 0, %B%, %B%(at_npi IN pParams[1].npis OR op_npi IN pParams[ […] #UNIQUENAME(report) %report% := pReportFunction(%K%); #UNIQUENAME(sorted) %sorted% := SORT(%report%, pSortByField); #UNIQUENAME(O1) %O1% := OUTPUT(pParameters, NAMED('Request')); oSummary := DATASET([{ COUNT(%sorted%) }], WS.Layouts.service_summary_layout); #UNIQUENAME(O2) %O2% := OUTPUT(oSummary, NAMED(‘Metadata')); #UNIQUENAME(O3) %O3% := OUTPUT( CHOOSEN(%sorted%, pParams[1].results, pParams[1].offset), NAMED(pServiceName), ALL); PARALLEL(%O1%, %O2%, %O3%); ENDMACRO;
- 51. 51 lpezet@archwayha.com www.linkedin.com/in/lucpezet mezzetin.blogspot.com HPCC Systems open source portal: http://hpccsystems.com Thank you Questions? Feedback? Questions ? Feedback ? www.linkedin.com/in/lucpezet