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LDBC Benchmarking Ontology

1 Intro

The Benchmarking Ontology (BM) will cover the following scope. For each scope area we list some relevant ontologies that we have taken inspiration from, or reused.

  • system under test (SUT)
    • hardware & price: GoodRelations, LinkedOpenCommerce
    • platform: DOAP, with dbpedia URLs for specific things (eg db:Linux, db:Java_Virtual_Machine)
    • database: DOAP (project and relase), FOAF (vendor, sponsor)
  • benchmark definition: RDFUnit, test manifests for DAWG (SPARQL), R2RML, RDF
    • benchmark definition versions
  • benchmark execution setup
    • driver, parameters, SUT
  • benchmark generator, parameters
    • benchmark dataset (if it makes sense to save rather than rerun generator): VOID
  • results provenance: PROV
  • detailed results log: RLOG
  • results/assertions for each query: EARL, RDFUnit
  • result statistics: CUBE, SDMX

This is a rather ambitious scope. Following partner priorities, we start by describing Result Statistics, which is a little backwards but anyway.

1.1 Revisions

  • <2015-01-21>: initial version. Overview of prefixes, potential benchmarks. Stat ontologies, Cube, SPB Sample1, BM Stat initial
  • <2015-02-02>: added discussion on Benchmark Result Dimensions Benchmark Result Dimensions
  • NEXT: SPB Stat sample

1.2 Prefixes

All the prefixes we use are in ./prefixes.ttl and should be:

  • Added to a repo (eg Ontotext GraphDb can do that from this file), or
  • Prepended to Turtle files before loading/validating

We use the prefix.cc service as much as possible. A lot of the used prefixes can be fetched from:

We include here a very brief description

prefixontologyused for
DCTDublin Core Termsvarious props, eg dct:extent of a Run
DBRDBpedia resourcestable URLs, eg dbr:Linux, dbr:Java_Virtual_Machine
DBODBpedia Ontology(maybe) a few properties
DOAPDescription of a Projectdatabase, release
GRGood Relationsprice (or maybe will prefer schema.org)
OWLWeb Ontology Languagesystem ontology
PROVProvenance Ontologyprovenance of results, start/end of Run
QUDTQuantities, Units, Dimensions and TypesUnits of Measure in stats
RDFResource Description Frameworksystem ontology
RDFSRDF Schemasystem ontology
Schemaschema.org ontologyvarious props
SKOSSimple Knowledge Organisation Systemconcepts and codelists (concept schemes)
SPINSPARQL Inferencing Notation(maybe) constraints on cube representation
UnitUnits of Measure (part of QUDT)Units of Measure in stats
XSDXML Schema Datatypesliteral datatypes
SDMX*Statistical Data and Metadata eXchangestatistical concepts

1.3 Testing Ontologies

The idea to represent tests and test runs in RDF is very old. We’ve studied a number of testing ontologies that have influenced greatly our design. Still, we couldn’t reuse a lot, because our domain is performance testing, not conformance testing.

Below are some brief descriptions, followed by more details in subsections. Legend:

  • x = well developed or widely used
  • ? = maybe will use
  • + = will use
sprefixontologycould be used for
xEARLEvaluation and Report Languagereporting conformance (pass/fail) claims
NIF-STCNIF test caseweak ontology for testing NLP Interchange Format
xRDB2RDF-TCRDB2RDF test casetesting RDB2RDF mapping language implementations
RDB2RDF-testRDB2RDF testtesting RDB2RDF. Missing
RDF-testRDF 1.1 testtesting RDF parsers
RDF-test1RDF test (old)testing RDF parsers
?result-setSPARQL result setcould be used in conformance/validation testing
+RLOGRDF Logging Ontologybasic log entry (timestamp, level, message)
xRUT*RDFUnit: test generation and excutiontest definitions and test results
test-dawgSPARQL query testing (old)
?test-descrTest Metadata, see working notePurpose, grouping, etc
+test-manifestTest Manifestrepresenting test cases
xtest-querySPARQL 1.1 query testing
test-updateSPARQL 1.1 update testing

1.4 Test Manifest

A test Manifest is a ttl description of a test suite (set of test cases), pointing to all relevant files (inputs, queries, “action” or expected output). Manifests are widely used by W3C working groups. Because test cases are made up mostly of files, it is notorious how well the directory and RDF structures are inter-meshed, and we should learn from this. Test cases and queries have stable identifiers, which are used as pivots in test reporting (see EARL).

Examples:

RDF 1.1
SPARQL/DAWG/SparqlScore
R2RML

1.5 EARL

EARL (Evaluation and Report Language) was first developed by the WAI Evaluation and Repair Tools Working Group, but is now used widely by W3C groups.

Most W3C specifications have an obligation to produce an Implementation Report that list at least 2 conformant implementations for every spec feature. This requires conformance testing, and EARL is designed to express conformance claims. By asking implementors to provide results in EARL, the implementation reports of numerous systems can be assembled automatically to a webpage. We want to use the same idea for the benchmark reporting section of the LDBC website.

Examples:

RDF 1.1
RDB2RDF
SPARQL

Report makers (HTML generators):

Test drivers (harness & EARL generators):

2 Potential Benchmarks

We follow an example-driven appproach: first make Turtle files for specific examples, then make an ontology to fit them. (Since we borrow liberally from other ontologies, in many cases we make what’s called Application Profiles, i.e. specifications about the shapes of our RDF.)

We may cover the following examples, listed in decreasing priority. Our intent is for BM to be able to represent all of these benchmarks

AbbrevBenchmarkMoSCoW
SPBSemantic Publishing Benchmarkmust
SNBSocial Network Benchmarkmust
BSBMBerlin SPARQL Benchmarkshould
SP2BSPARQL 2 Benchmarkcould
LUBMLUBM Lehigh University Benchmarkcould
TPC-HTransaction Processing Council Hwon’t

2.1 TODO SPB

Update description & links

2.2 TODO SNB

Update description & links

2.3 BSBM 3.1

2.4 LOD2 Cluster

Description of sophisticated hardware:

2.5 StarDog results

Succinct sheet describing results for BSBM, LUBM, SP2B:

Nice variety but little detail

2.6 TPC-H

Tons of detial, maybe not so relevant for us. Each run has representations at these levels of detail:

  • One line 
    Rank, Company, System, QphH, Price/QphH, Watts/KQphH, System Availability, Database, Operating System, Date Submitted, Cluster
  • Executive Summary: eg 13 pages
  • Full Disclosure Report: eg 37 pages
  • Supporting Files: 6Mb to 3Gb(!): won’t look at them

Results:

3 BM Statistics

The most important output of BM is the statistical representation of benchmark results.

3.1 Stats Terms

It may be hard for someone without stats background to understand stats ontologies, so we provide first some terms from stats/OLAP. Pleae note that these terms are slanted towards teh Cube ontology. The key terms are Dimension, Attribute, Measure.

  • Cube: a multidimensional data structure carrying Observations
  • Observation: a value plus a number of Components that help make sense of it
  • Component: any of Dimension, Attribute or Measure; the facets defining the structure of a cube
  • Data Structure Definition: the set of components defining a cube.
  • Dimension: identifies the observations: where the observations lie
    • In a cube, all observations must have the same dimensions (no nulls are allowed), but some shortcuts/normalization are allowed
  • Attribute: qualify and interpret the value, eg: unit of measurement, estimated, provisional
  • Measure: carries the observed value: what the values are
  • measureType Dimension: a Dimension defining which Measure is applicable to an Observation (like a tag/discriminator in a Tagged Union)
  • Slice: a cube subset where some of the Dimensions are fixed. Allows more economical cube description, and views over cubes (eg time series)

3.2 Stats Ontologies

We’ve looked at a number of stats ontologies, described in subsections below (the ones we use are described last). Legend:

  • x = well developed or widely used
  • ? = maybe will use
  • + = will use
sprefixontologycould be used for
DiscoDDI RDF Discovery Vocabulary (Data Documentation Initiative)Detailed representation of stats, questions, cases..
+QBRDF Data Cube Vocabulary“Canonical” stats ontology (SCOVO is the older version)
?QB4OLAPCube for OLAP, see Data Warehouse Systems: Design and Implementation sec 14.3.2 p.557Cube can’t represent hierarchical dimensions
+SDMXStatistical Data and Metadata eXchangecommon stat concepts, attributes, dimensions
?SStatDDI Controlled Vocabularies - SummaryStatisticConcepts for summary stats (min, max, mean…)
XKOSExtended Knowledge Organisation SystemSKOS extension with statistical levels

3.2.1 270a

The site http://270a.info/ is a treasure trove of deployed datasets, patterns, codelists, etc. It includes stats data from some 10 national and international stats offices, including Eurostat, ECB, WB, FAO, etc.

Interesting articles:

Tool

Eg this is how I found they have concepts for Percentile:

<http://worldbank.270a.info/property/percentile> a qb:DimensionProperty , rdf:Property ;
  rdfs:label   "Percentile"@en;
  rdfs:range  <http://worldbank.270a.info/classification/percentile>;
  qb:codeList <http://worldbank.270a.info/classification/percentile>.
<http://worldbank.270a.info/classification/percentile/90> a skos:Concept;
  skos:inScheme <http://worldbank.270a.info/property/percentile>.

3.2.2 Disco

Disco looks very promising, and has detailed in-depth stats examples (a lot more elaborate than Cube). It says “Disco only describes the structure of a dataset, but is not concerned with representing the actual data in it”. But in fact the examples show data representation as well.

3.2.3 DDI CV, SStat

DDI Controlled Vocabularies provides a number of codelists for common stats concepts.

3.2.3.1 Summary Statistics

In particular, Summary Statistics is relevant for us:

This is a promising vocabulary and is worth watching. But our current representation doesn’t use it because:

  • These codelists are not deployed yet (the namespace does not resolve)
  • We need 95 percentile and 99 percentile but SStat defines “OtherPercentile”, so we’d still need to extend or tack a number somewhere

3.2.4 SDMX

SDMX is an ISO spec providing common stats concepts and components (dimensions, attributes and measures). Originally defined in XML and EDI, it’s also translated to RDF. SDMX depends on Cube, but Cube may be used without SDMX.

Since the same concept (eg Gender) can be used in various roles (eg a Dimension or a Measure), skos:Concepts are used to tie them together. A component that is a qb:CodedProperty may also link to a qb:codeList (a skos:ConceptScheme or ad-hoc qb:HierarchicalCodeList).

Say we want to provide a Dimension describing Summary Stats (mean, min, max, etc). We define a property bm-stat:dimStat and tie it up to the concept bm-stat:conceptStat and a codeList bm-stat:stat:

bm-stat:dimStat a rdf:Property, qb:DimensionProperty, qb:CodedProperty;
  rdfs:label "Stat"@en;
  rdfs:comment "Statistic being measured (eg min, max)"@en;
  rdfs:range bm:Stat;
  qb:concept bm-stat:conceptStat;
  qb:codeList bm-stat:stat.

We also define a class bm-stat:Stat that’s co-extensive with the codeList bm-stat:stat, to allow rdfs:range declaration on the DimensionProperty:

bm-stat:stat a skos:ConceptScheme;
  rdfs:label "Summary Statistics scheme"@en;
  rdfs:comment "Single number representation of the characteristics of a set of values"@en;
  rdfs:seeAlso bm-stat:Stat.

bm-stat:Stat a rdfs:Class, owl:Class;
  rdfs:label "Stat"@en;
  rdfs:comment "Codelist (enumeration) of Summary Statistics concepts, eg min, max"@en;
  rdfs:subClassOf skos:Concept;
  rdfs:seeAlso bm-stat:stat.

Finally, we define the individual values as both instances of the class, and skos:inScheme of the codeList:

bm-stat:min a skos:Concept, bm-stat:Stat ;
  rdfs:label "Min"@en;
  rdfs:comment "Minimum value of an observation"@en;
  skos:inScheme bm-stat:stat.

It is tedious to define all these interlinked entities (a consistent naming approach is essential!) Such detailed self-description allows sophisticated cube exploration UIs and SPARQL query generation (rumor has it). However, we think it would be easier to develop queries by hand, so we may forgo the use of SDMX in future releases.

3.2.5 Cube

Cube is the “canonical” stats ontology adopted by W3C. It can work together or without SDMX.

There are many important parts to the specification, but we highlight only a couple in this section, and a more technical one in the next section.

Multiple Measures
If you need Observations that have several different Measures, there are several approaches:
  • Multi-measure observations. Each observation has the same set of measures, and attributes can’t be applied separately. 
    eg:o1 a ob:Observation;
  eg:attrUnit unit:MilliSecond;
  eg:measure1 123;
  eg:measure2 456.
  • Measure dimension. Each observation has one applicable measure, selected by qb:measureType (as a tag/discriminator in a in a Tagged Union). Different attributes can be applied. This is a more regular approach, recommended by SDMX. 
    eg:o1 a ob:Observation;
  eg:attrUnit unit:MilliSecond;
  qb:measureType eg:measure1;
  eg:measure1 123.
eg:o2 a ob:Observation;
  eg:attrUnit unit:Second;
  qb:measureType eg:measure2;
  eg:measure2 456.
  • Structured observation. You could put several values in one node, but then cannot Slice them independently 
    eg:o1 a ob:Observation;
  eg:attrUnit unit:MilliSecond;
  eg:measure [eg:value1 123; eg:value2 456].
Data Structure Definition (DSD)
The structure of a Cube is described with a DSD. The same DSD is normally reused between many Cubes with the same structure (eg a SNB DSD will be used by the stats cubes of all SNB Runs). A DSD is created by listing the qb:components that apply to a cube, and optionally defining SliceKeys. Consistent naming of different kinds of components (eg dim, attr, meas) is essential to facilitate understanding. Eg 
snb-stat:dsd a qb:DataStructureDefinition;
  ob:component [qb:dimension bm-stat:dimScaleFactor],  # dataset size
  ob:component [qb:dimension bm-stat:dimStat],         # mean, min, max, ...
  ob:component [qb:attribute bm-stat:attrUnit],        # MilliSecond, Second, ...
  ob:component [qb:dimension qb:measureType],          # discriminator for the rest
  ob:component [qb:measure   bm-stat:measRuntime],     # observe Runtime, or
  ob:component [qb:measure   bm-stat:measDelayTime].   # observe DelayTime
componentAttachment
Every Observation must have defined values for all Dimensions and all mandatory Attributes. However, Cube allows some shortcuts by letting you specify a Dimension/Attribute at the level of the cube, slice, or a Measure. This last option is unclear in the spec, see my forum posting and the next section.

3.2.6 Cube Normalization

If you specify property qb:componentAttachment with of the values qb:DataSet, qb:Slice, qb:MeasureProperty for a Dimension/Attribute, then you fix the value for that Dimension/Attribute at the corresponding higher level, not in the Observation. For example (not showing qb:DataSet for brevity):

eg:myDSD a qb:DataStructureDefinition;
  qb:component [qb:measure eg:measure1 ];
  qb:component [qb:measure eg:measure2 ];
  qb:component [qb:attribute eg:measUnit; qb:componentAttachment qb:MeasureProperty].

eg:measure1 a qb:MeasureProperty;
  eg:measUnit unit:Percent .

eg:measure2 a qb:MeasureProperty;
  eg:measUnit unit:Number .

eg:observation1 a qb:Observation;
  eg:measure1 55;   # Percent
  eg:measure2 1333. # Number

This allows abbreviated (more economical) cube representation. But to simplify SPARQL queries and Integrity constraint checking, a Normalization Algorithm is defined that expands (flattens) the cube by transferring the values from the higher level to each Observation.

The algorithm is defined in terms of SPARQL updates (INSERT WHERE).

  • Phase 1 are normal RDFS rules
  • Phase 2 are the Cube-specific rules.

Unfortunately, the above case won’t be handled by Phase 2, since it shows only attachment to qb:DataSet or qb:Slice.

We find an extra fourth rule commented-out at the original source https://code.google.com/p/publishing-statistical-data/source/browse/trunk/src/main/resources/flatten.ru (in this case ru is the extension for SPARQL Update):

# Measure property attachments
  INSERT {
      ?obs  ?comp ?value
  } WHERE {
      ?spec  qb:componentProperty ?comp ;
             qb:componentAttachment qb:MeasureProperty .
      ?dataset qb:structure [qb:component ?spec] .
      ?comp    a qb:AttributeProperty .
      ?measure a qb:MeasureProperty;
               ?comp ?value .
      ?obs     qb:dataSet ?dataset;
               ?measure [] .
  }

It transfers from a Measure to an Observation, iff:

  • An Attribute ?comp is attached to a MeasureProperty,
  • The Measure is used for the Observation
  • The attribute is declared to have qb:componentAttachment qb:MeasureProperty. To see this, it helps to rewrite the WHERE clause like this (qb:component is super-property of qb:attribute): 
    ?dataset qb:structure [a qb:DataStructureDefinition;
  qb:component
    [qb:attribute ?attr; qb:componentAttachment qb:MeasureProperty]].
?attr    a qb:AttributeProperty .
?measure a qb:MeasureProperty;
  ?attr ?value .
?obs a qb:Observation;
  qb:dataSet ?dataset;
  ?measure ?measValue.

3.2.7 Normalization with Ontotext GraphDb Rules

INSERT WHERE works fine for static/small datasets, but what if you have a huge Cube that’s updated incrementally? (Eg a cube to which observations are being added by a streaming benchmark driver). Ontotext GraphDb rules work better in such situation, since they allow you to insert and delete triples freely, while maintaining consistency.

The script ./cube-normalize.pl takes a .ru file as described above and produces a rule file ./cube-normalize.pie (in addition, a RDFS rules file needs to be loaded or merged with this one). Eg the Measure property attachments INSERT WHERE rule from the previous section is translated to this rule:

Id: qb2_Measure_property_attachments
  spec  <qb:componentProperty> comp
  spec  <qb:componentAttachment> <qb:MeasureProperty>
  dataset <qb:structure> struc
  struc   <qb:component> spec
  comp    <rdf:type> <qb:AttributeProperty>
  measure <rdf:type> <qb:MeasureProperty>
  measure comp value
  obs     <qb:dataSet> dataset
  obs     measure blank
  --------------------------
  obs  comp value

In addition, it adds an inverse propertyChainAxiom for the loop between DataSet, Slice and Observation (see the Cube domain model):

Id: qbX_slice_observation_dataSet
  dataset <qb:slice>       slice
  slice   <qb:observation> obs
  --------------------------------
  obs     <qb:dataSet>     dataset

This allows you to skip qb:dataSet for an Observation that’s already attached to a Slice of the cube using qb:observation.

Note: “qb2” stands for “Cube Phase2 normalization”, and “qbX” stands for “I’m too lazy to repeat myself”.

3.2.8 Benchmark Result Dimensions

We will document all particulars of a benchmark run in bm:Run, including:

  • Full hardware and software details of the System Under Test
  • URLs of configuration files of the System Under Test, test driver, etc
  • RDF nodes with property-value for important configuration parameters

In contrast, the Benchmark Result examples (eg SNB Turtle below) as of <2015-01-21> use a really minimal set of dimensions:

  • dimQuery states which query (or Total) the measurement pertains to
  • dimStat states which Summary Statistic Summary Statistics (eg mean, min, max) is expressed by the measurement

To compare or chart numbers across different Runs (varying eg database, release, database settings, hardware, benchmark version), we need to use more of the Run parameters as cube Dimensions.

The down-side of every dimension is that it not only adds a triple to every observation, but also multiplies the number of observations through Cube Normalization Cube Normalization. Eg assume you have a cube with D dimensions, O observations and (D+X)*O triples (where X is proportional to the number of measures and attributes) and you add an D+1’th dimension with Y values. You’ll end up with O*Y observations and (D+X+1)*O*Y triples.

So what are the important benchmark Run parameters to add as Dimensions? Currently proposed:

  • scaleFactor: to compare performance against dataset size
  • database release: to compare the evolution of a database in time
  • database: to compare across databases (note: this is implied by “database release”, so we could spare it)
  • RAM size (Gb): a key hardware parameter

How about these?

  • Loading parameters such as number of agents/threads (SNB threadCount), SNB timeCompressionRatio, SNB gctDeltaDuration. IMHO the benchmark sponsor is supposed to optimize these until maximum database performance is achieved, so we don’t compare across them
  • CPU and Disk performance. But is there a standardized way to report them?
  • query mix, eg which queries are enabled, whether analytical queries were included, query interleave times, etc. The number and times for each query type are reported through dimQuery, but the mix as a whole also affects the performance of each query, so maybe we need to capture this. But how? A query mix is a complex structure in itself…
  • SUT platform such as operating system, JVM etc: it’s possible (but maybe not very likely) we’d want to compare against such factors
  • Total SUT price. TCP captures that (and queries per second per dollar), so maybe we should too

In contrast, we don’t need to capture the folowing as Dimensions:

  • benchmark: can’t compare across benchmarks (can’t compare apples to oranges)
  • benchmark version: this is a key parameter of a Run, but again we can’t compare apples to oranges
  • driver version: an important parameter of a Run, but it’s not supposed to affect benchmark performance
  • dataset parameters such as dictionaries used, network distributions, literal distributions, etc.

3.3 SNB Sample1

The SNB spec LDBC_SNB_v0.2.0 sec 3.3 “Gathering the results” provides the example ./snb-sample1.json:

"name": "Query1",
"count": 50,
"unit": "MILLISECONDS",
"run_time": {
  "name": "Runtime",
  "unit": "MILLISECONDS",
  "count": 50,
  "mean": 100,
  "min": 2,
  "max": 450,
  "50th_percentile": 98,
  "90th_percentile": 129,
  "95th_percentile": 432,
  "99th_percentile": 444
},
"start_time_delay": {
  "name": "Start Time Delay",
  "unit": "MILLISECONDS",
  "count": 7,
  "mean": 3.5714285714285716,
  "min": 0,
  "max": 25,
  "50th_percentile": 0,
  "90th_percentile": 0,
  "95th_percentile": 25,
  "99th_percentile": 25
},
"result_code": {
  "name": "Result Code",
  "unit": "Result Code",
  "count": 50,
  "all_values": {
    "0": 42,
    "1": 8
  }
}

It provides stats for 50 executions of Query1 along 3 measures:

  • Runtime: query execution time
  • StartDelay: delay between scheduled and actual query start time.
  • Result: result code

Note: queries are scheduled by the driver using these parameters:

  • LdbcQueryN_interleave: interval between successive executions of query N
  • timeCompressionRatio: multiplier to compress/stretch all interleave times
  • toleratedExecutionDelay: if start delay exceeds this, a timeout is recorded

These measures are interesting, since:

  • We have 2 numeric measures (MilliSeconds) and 1 categorial (result code)
  • The numeric measures provide a number of Summary Statistics

3.3.1 SNB Turtle

We represent this as the following Turtle.

  • We populate the cube using 3 Slices, each having the same structure snb-stat:sliceByQueryAndMeasure
  • We model the Summary Statistics as Dimension (bm-stat:dimStat), and the unit-of-measure as Attribute (bm-stat:attrUnit)
  • For the categorial measure snb-stat:measResult we model the individual categories (code values) as Attrbute (bm-stat:attrResult)
snb-run:sample1-cube a qb:DataSet;
  qb:structure snb-stat:dsdCube;
  qb:slice snb-run:sample1-sliceRuntime, snb-run:sample1-sliceStartDelay, snb-run:sample1-sliceResult.

snb-run:sample1-sliceRuntime a qb:Slice;
  qb:sliceStructure snb-stat:sliceByQueryAndMeasure;
  snb-stat:dimQuery snb:Query1;
  qb:measureType qb:measRuntime;
  qb:observation
    [ bm-stat:dimStat bm-stat:count;        bm-stat:measRuntime  50; bm-stat:attrUnit unit:Number      ],
    [ bm-stat:dimStat bm-stat:mean;         bm-stat:measRuntime 100; bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:min;          bm-stat:measRuntime   2; bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:max;          bm-stat:measRuntime 450; bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:median;       bm-stat:measRuntime  98; bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:percentile90; bm-stat:measRuntime 129; bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:percentile95; bm-stat:measRuntime 432; bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:percentile99; bm-stat:measRuntime 444; bm-stat:attrUnit unit:MilliSecond ].

snb-run:sample1-sliceStartDelay a qb:Slice;
  qb:sliceStructure snb-stat:sliceByQueryAndMeasure;
  snb-stat:dimQuery snb:Query1;
  qb:measureType snb-stat:measStartDelay;
  qb:observation
    [ bm-stat:dimStat bm-stat:count;        bm-stat:measStartDelay  7;    bm-stat:attrUnit unit:Number      ],
    [ bm-stat:dimStat bm-stat:mean;         bm-stat:measStartDelay  3.57; bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:min;          bm-stat:measStartDelay  0;    bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:max;          bm-stat:measStartDelay 25;    bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:median;       bm-stat:measStartDelay  0;    bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:percentile90; bm-stat:measStartDelay  0;    bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:percentile95; bm-stat:measStartDelay 25;    bm-stat:attrUnit unit:MilliSecond ],
    [ bm-stat:dimStat bm-stat:percentile99; bm-stat:measStartDelay 25;    bm-stat:attrUnit unit:MilliSecond ].

snb-run:sample1-sliceResult a qb:Slice;
  qb:sliceStructure snb-stat:sliceByQueryAndMeasure;
  snb-stat:dimQuery snb:Query1;
  qb:measureType snb-stat:measResult;
  qb:observation
    [ bm-stat:dimStat bm-stat:count; bm-stat:measResult 50;  bm-stat:attrResult snb-stat:result-total ],
    [ bm-stat:dimStat bm-stat:count; bm-stat:measResult 42;  bm-stat:attrResult snb-stat:result-0 ],
    [ bm-stat:dimStat bm-stat:count; bm-stat:measResult  8;  bm-stat:attrResult snb-stat:result-1 ].

I hope this representation fairly obviously corresponds to the JSON. Please comment.

Possible extensions:

  • More dimensions, see Benchmark Result Dimensions
  • May need some hierarchical dimension logic to capture the relation between Query Mix and individual Queries

Converting from JSON to Turtle should not be hard. We might even be able to convert automatically by using a JSONLD Context, but I have not tried it.

3.3.2 SNB Header

The JSON also has a small “Header”:

"unit": "MILLISECONDS",
"start_time": 1400750662691,
"finish_time": 1400750667691,
"total_duration": 5000,
"total_count": 50,

I thought about representing this as a small cube, but decided it’s overkill. So I hacked something using duct tape from various vocabularies (PROV, DCT, RDF). Actually there is some thought invested in here:

  • PROV will be used significantly to describe Runs: who, when, what entities were used (eg benchmark definition, SUT, etc)
  • The general pattern “propName-value-unit” will be used throughout, eg for hardware features, benchmark parameters, etc
snb-run:sample1 a bm:Run;
  prov:startedAtTime [rdf:value 1400750662691; qudt:unit unit:MilliSecond];
  prov:endedAtTime   [rdf:value 1400750667691; qudt:unit unit:MilliSecond];
  dct:extent         [rdf:value 5000;          qudt:unit unit:MilliSecond];
  dct:extent         [rdf:value 50;            qudt:unit unit:Number];
  # TODO: describe benchmark, driver, system under test, etc
  bm-stat:dataset snb-run:sample1-cube.

Notes:

  • The most important property is the link bm-stat:dataset snb-run:sample1-cube to the cube.
  • The Run needs a lot more contextual links (see “PROV” above)
  • Using dct:extent twice for such varied things like Duration and Count may seem weird, but it matches its definition “size or duration of the resource”, and Unit distinguishes between the two.

3.3.3 SNB SPARQL

To make some charts, we need to extract data with SPARQL. Given a Run, say we want to extract:

  • Each Runtime observation
  • Each Query, which will be the series. Assume snb:Query has sortable dc:identifier (eg 1 or “Q001”)
  • Mean, min, max to plot “line with error bars”
  • A “query fulfillment ratio” being “Query runtime count” divided by “Run total count”

Since Cube Normalization has brought all values down to each Observation, this is easy. Since there are no nulls, we don’t need OPTIONALs, so it’s also fast. We assume that the SPARQL variable $Run is instantiated (i.e. it’s a SPARQL parameter)

select ?query ?mean ?min ?max ?fulfillmentRatio {
  $Run bm-stat:dataSet ?dataset;
       dct:extent [rdf:value ?runCount; qudt:unit unit:Number].
  ?obs qb:dataSet ?dataset; qb:measureType qb:measRuntime;
       snb-stat:dimQuery [dc:identifier ?query].
  {?obs bm-stat:dimStat bm-stat:count; bm-stat:measRuntime ?count.
      bind(?count / ?runCount as ?fulfillmentRatio)} union
  {?obs bm-stat:dimStat bm-stat:mean;  bm-stat:measRuntime ?mean} union
  {?obs bm-stat:dimStat bm-stat:min;   bm-stat:measRuntime ?min} union
  {?obs bm-stat:dimStat bm-stat:max;   bm-stat:measRuntime ?max}
} order by ?query
  • Note: op:numeric-divide() is xsd:decimal if both operands are xsd:integer, so we don’t need to coerce to decimal
  • TODO: check how the UNION behaves

3.3.4 SNB Stat Ontology

./snb-stat.ttl is based on BM Stat BM Stat Ontology, and includes some Stat things that are specific to SNB (we could decide to move it into BM Stat to keep the benchmarks-specific ontology minimal).

First a more specific Dimension that inherits all fields from bm-stat:dimQuery but fixes the range to snb:Query. This allows checking that the right query is used in SNB cubes, but that’s little gain. We can do without this property.

snb-stat:dimQuery a rdf:Property, qb:DimensionProperty;
  rdfs:label "query"@en;
  rdfs:comment "Query being measured"@en;
  rdfs:subPropertyOf bm-stat:dimQuery;
  rdfs:range snb:Query;
  qb:concept bm-stat:conceptQuery.

Then a Measure for the SNB-specific concept of “start time delay”:

snb-stat:measStartDelay a rdf:Property, qb:MeasureProperty;
  rdfs:label "start delay"@en;
  rdfs:comment "Delay from scheduled time to actual execution time"@en;
  rdfs:range xsd:decimal.

Then we define a concept of “Result (code)”, and an Attribute and Dimension using that concept. You can see how the Attribute and Dimension are tied together through the concept. The Attribute is categorial (a qb:CodedProperty) while the Measure is numeric (integer).

snb-stat:attrResult a rdf:Property, qb:AttributeProperty, qb:CodedProperty;
  rdfs:label "result code"@en;
  rdfs:comment "Result being counted"@en;
  rdfs:range snb-stat:Result;
  qb:concept bm-stat:conceptResult;
  qb:codeList snb-stat:result.

snb-stat:measResult a rdf:Property, qb:MeasureProperty;
  rdfs:label "result count"@en;
  rdfs:comment "Count of results"@en;
  qb:concept bm-stat:conceptResult;
  rdfs:range xsd:integer.

We also define a codeList and code values (concepts) like snb-stat:result-1 (not interesting).

Now we define a DataStructureDefinition for the cube. We use Measure dimension qb:measureType because we got heterogenous observations: the 3 Measures are not uniformly populated throughout the cube.

snb-stat:dsdCube a qb:DataStructureDefinition;
  qb:component 
    [ qb:dimension snb-stat:dimQuery; qb:componentAttachment qb:Slice ],
    [ qb:dimension qb:measureType; qb:componentAttachment qb:Slice ],
    [ qb:dimension bm-stat:dimStat ], # mean, min, max, etc
    [ qb:attribute bm-stat:attrUnit ], # applicable for measRuntime and measStartDelay
    [ qb:attribute bm-stat:attrResult ], # applicable for measResult
    [ qb:measure   bm-stat:measRuntime ],
    [ qb:measure   snb-stat:measStartDelay ],
    [ qb:measure   snb-stat:measResult ];
  qb:sliceKey snb-stat:sliceByQueryAndMeasure.

Finally we define a slice structure. In each slice instance, snb-stat:dimQuery and ~qb:measureType must be fixed.

snb-stat:sliceByQueryAndMeasure a qb:SliceKey;
  rdfs:label "slice by query and measure"@en;
  rdfs:comment "Fix dimensions dimQuery and measureType"@en;
  qb:componentProperty snb-stat:dimQuery, qb:measureType.

Please look at SNB Turtle and check how this structure is used by the cube and slice instances.

3.3.5 TODO SNB FDR

Map c:/my/Onto/proj/LDBC/benchmarks/snb_full_disclosure/full_disclosure.txt

3.4 SPB Results

semantic_publishing_benchmark_results.log is a simple text format like this:

  • It’s cumulative, so you only need to look at the last block
  • 960260 is the timestamp in MilliSeconds (with warmup), 900 is the timestamp in seconds (without warmup)
  • Editorial are write threads (just 1); Aggregation are read threads (6 of them)
  • Write threads execute 3 kinds of queries (insert, update, delete), read threads execute Q1..Q9
  • Counts per update operation, per query; total updates and total queries
  • “Completed query mixes” is just about equal to the minimum of counts per query (a mix is counted Completed if each query was executed once)
  • Number of errors: total for update operations; per query for read operations
  • Average, min, max MilliSeconds per operation (90, 90, 99 percentiles will also be added)

./spb-sample1.txt:

960260 : 

Seconds : 900 (completed query mixes : 296)
	Editorial:
		1 agents

		7082  inserts (avg : 85      ms, min : 50      ms, max : 1906    ms)
		903   updates (avg : 203     ms, min : 128     ms, max : 1894    ms)
		879   deletes (avg : 110     ms, min : 64      ms, max : 1397    ms)

		8864 operations (7082 CW Inserts (0 errors), 903 CW Updates (0 errors), 879 CW Deletions (0 errors))
		9.8489 average operations per second

	Aggregation:
		6 agents

		299   Q1   queries (avg : 2120    ms, min : 10      ms, max : 31622   ms, 0 errors)
		297   Q2   queries (avg : 13      ms, min : 10      ms, max : 108     ms, 0 errors)
		297   Q3   queries (avg : 3200    ms, min : 383     ms, max : 85870   ms, 0 errors)
		298   Q4   queries (avg : 694     ms, min : 100     ms, max : 7135    ms, 0 errors)
		300   Q5   queries (avg : 368     ms, min : 16      ms, max : 5622    ms, 0 errors)
		298   Q6   queries (avg : 303     ms, min : 37      ms, max : 10246   ms, 0 errors)
		297   Q7   queries (avg : 1439    ms, min : 58      ms, max : 4995    ms, 0 errors)
		297   Q8   queries (avg : 531     ms, min : 80      ms, max : 2293    ms, 0 errors)
		298   Q9   queries (avg : 9184    ms, min : 509     ms, max : 37868   ms, 0 errors)

		2681 total retrieval queries (0 timed-out)
		3.0225 average queries per second

3.4.1 TODO SPB Turtle

3.5 BM Stat Ontology

The BM Stat Ontology ./bm-stat.ttl defines common stat concepts that can be used between different benchmarks:

  • Common concepts, such as Run, Runtime, Query, Result (code)
  • Summary Statistics codeList bm-stat:stat, class bm-stat:Stat and code values, as shown in SDMX
  • Commonly used dimensions, measures and attributes: bm-stat:dimQuery, bm-stat:dimStat, bm-stat:measRuntime, bm-stat:attrUnit.
    • These have appropriate ranges: (to be defined in subproperties), bm-stat:Stat, xsd:decimal, qudt:Unit respectively

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