1. OLAP Querying of Document Stores
in the Presence of Schema Variety
Matteo Francia, Enrico Gallinucci, Matteo Golfarelli, Stefano Rizzi
{m.francia, enrico.gallinucci, matteo.golfarelli, stefano.rizzi} @ unibo.it

2. Introduction
Schemaless databases (e.g., document stores) are preferred for storing
heterogeneous data with variable structural forms
• Missing or additional fields
• Different names or types for a field
• Different structuresfor instances
Absence of a unique schema
• More flexibility to operational applications, more complexity to analytical applications
Adopting a classical DW design approach requires notable effort
• Understand the rules that drove the use of alternative schemas
• Integrate to identify a common schema to be adopted for analysis
Enrico Gallinucci – University of Bologna SEBD 2020 2

3. Our proposal
An approach to OLAP querying on document stores
• Embrace and exploit the inherent variety of documents
• Inclusive solution: the user can query a concept
even if it is present in a subset of documents only
Enrico Gallinucci – University of Bologna SEBD 2020 3
Document store
Schema extraction
Schema integration
FD enrichment
Querying
Dependency graph
Global schema, Mappings
Local schemas
Formulation
Validation
Reformulation
Translation
Execution
Merge

4. Case study
Real-world collection of workout sessions
• MongoDB 3.4
• 6 local schemas
• 5M workout sessions
• 35M exercises
• 85M sets
Enrico Gallinucci – University of Bologna SEBD 2020 4
[ { "_id" : ObjectId("54a4332f44"),
"User" :
{ "FullName" : ”John Smith",
"Age" : 42 },
"StartedOn" : ISODate("2017-06-15"),
"Facility" :
{ "Name" : "PureGym Piccadilly",
"Chain" : "PureGym" },
"SessionType" : "RunningProgram",
"DurationMins": 90,
"Exercises" :
[ { "Type" : "Leg press",
"ExCalories" : 28,
"Sets" :
[ { "Reps" : 14,
"Weight" : 60 },
. . .
] },
{ "Type" : "Tapis roulant" },
. . .
]
} ,
. . .
]

5. Schema extraction
Goal: identify the set of distinct local schemas that occur inside a
collection of documents
• Completely automatic stage
Enrico Gallinucci – University of Bologna SEBD 2020 5
Document store
Schema extraction
Schema integration
FD enrichment
Querying
Dependency graph
Global schema, Mappings
Local schemas

6. Schema integration
Goal: integrate the local schemas to obtain a single, global schema
• A preliminary global schema is defined as the name-based union of all local
schemas (automatic)
• The user refines the preliminary global schema by merging matching (sets of)
fields (pay-as-you-go)
• Existing tools (e.g., Coma 3.0) can be used to
automatically find candidate matches
Enrico Gallinucci – University of Bologna SEBD 2020 6
Document store
Schema extraction
Schema integration
FD enrichment
Querying
Dependency graph
Global schema, Mappings
Local schemas

7. Goal: propose a MD view of the global schema to enable OLAP analyses
• Identification of hierarchies → Identification of functional dependencies (FDs)
• Some exact FDs can be derived from the global schema
• Additional FDs can only be found by querying the data
• DSs may contain incomplete and faulty data
• We look for approximate FDs (AFDs)
• Efficient strategy for AFD detection [1]
Schema enrichment
Enrico Gallinucci – University of Bologna SEBD 2020 7
Document store
Schema extraction
Schema integration
FD enrichment
Querying
Dependency graph
Global schema, Mappings
Local schemas
[1] Golfarelli, Graziani, Rizzi: Starry vault: Automating multidimensional modeling from data vaults. In: Proc. ADBIS (2016)

8. Schema enrichment (example)
Enrico Gallinucci – University of Bologna SEBD 2020 8
Exercises.Type
Exercises.Sets.Weight
Exercises.Sets.Reps
_id
User.FullName
SessionType
Facility.Name
Facility.City
Facility.ChainUser.Age
User.FirstName
User.LastName DurationMins
Exercises._id
Exercises.Sets. _id
Exercises.ExCalories
Exercises.Sets.SetCalories
level (support=1)
level (support<1)
FD
AFD
Facility.Country
The result is a dependency graph, providing a MD view in terms of FDs

9. Querying (1)
Goal: enable the formulation of OLAP queries on the dependency graph
and their execution on the collection
• Validate against well-formedness requirements in the literature
• There must exist a field that functionally determines all the others (the fact)
• Functional independence in group-by set
• Disjointess:the granularity of measures
should be finer than the one of the
group-by fields (or: double-counting)
• Completeness:the group-by fields
should have full support
(balancing strategies are used)
Enrico Gallinucci – University of Bologna SEBD 2020 9
Document store
Schema extraction
Schema integration
FD enrichment
Querying
Formulation
Validation
Reformulation
Translation
Execution
Merge

10. Querying (example)
q = < {Facility.City; Exercises.Type}; (User.Age≥ 60); Exercises.Sets.Weight;avg >
Enrico Gallinucci – University of Bologna SEBD 2020 10
db.WS.aggregate({
{ $unwind: "$Exercises" },
{ $unwind: "$Exercises.Sets" },
{ $match: { "User.Age": { $gte: 60 } } },
{ $project: {
"Facility.City": { $ifNull: ["$FacilityCity","$FacilityName"] } },
"Exercises.Type": 1,
"Exercises.Sets.Weight": 1,
"balanced": { $cond: ["$FacilityCity",false,true] } } },
{ $group: {
" id": {
"FacilityCity", "$FacilityCity",
"ExercisesType", "$Exercises.Type",
"balanced", "$balanced" },
"Exercises.Sets.Weight": { $avg: "$Exercises.Sets.Weight" },
"count": { $sum: 1 },
"count-m": { $sum: f $cond: ["$Exercises.Sets.Weight",1,0] } } } } } )
Exercises.Type
Exercises.Sets.Weight
Exercises.Sets.Reps
_id
User.FullName
SessionType
Facility.Name
Facility.City
Facility.ChainUser.Age
User.FirstName
User.LastName DurationMins
Exercises._id
Exercises.Sets. _id
Exercises.ExCalories
Exercises.Sets.SetCalories
level (support=1)
level (support<1)
FD
AFD
Facility.Country

11. Goal: enable the formulation of multidimensional queries on the
dependency graph and their execution on the collection
• Reformulate into multiple queries, one for each local schema
• Rely on a previous query reformulation approach for federated DWs
• Translate into the query language of the DS
• Multi-stage pipelines on MongoDB
• Execute the queries and
merge the results
• Performed in-memory
• OLAP queries usually produce limited results
• Transcoding functions homogenize values Document store
Schema extraction
Schema integration
FD enrichment
Querying
Formulation
Validation
Reformulation
Translation
Execution
Merge
Querying (2)
Enrico Gallinucci – University of Bologna SEBD 2020 11

12. Future work
• Increase the efficiency of the querying phase
• Optimize query reformulation to issue a single but more complex query
• Broaden the scope of the approach
• Multiple collections and multiple store with different data models
• Evaluate alternative execution engines (e.g., big data tools)
• Build a fully working prototype
• Introduction of online repairing of AFDs
• Automatically repair the errors in AFDs at query time
• Directly receive correct results without need to modify the original data
Enrico Gallinucci – University of Bologna SEBD 2020 12

13. Thank you
Questions?

14. Query evaluation
• Level density
• Necessary when the group-by fails the completeness constraint
• Evaluate the impact of the balancing strategy
• Measure density
• Necessary when the measure of the query does not have full support and the
results offer only a partial summary
• Evaluate the precision of result depending on how many documentscontain
the measure
• Accuracy
• Necessary when a new query is formulated during an OLAP session
• Quantify the accuracy of results w.r.t. those obtained from the previous query
Enrico Gallinucci – University of Bologna SEBD 2020 14

15. Query execution times
Execution times of three queries (qa, qb, and qc) split into the execution
times of the required local queries
Enrico Gallinucci – University of Bologna SEBD 2020 15

16. Schema extraction (example)
Enrico Gallinucci – University of Bologna SEBD 2020 16
_id
User.FullName
User.Age
StartedOn
Facility.Name
Facility.Chain
SessionType
DurationMins
Sets_id
Reps
Weight
Exercises_id
Type
ExCalories
WS
Sets
Exercises
{ "_id" : ObjectId("54a4332f44"),
"User" :
{ "FullName" : ”John Smith",
"Age" : 42 },
"StartedOn" : ISODate("2017-06-15"),
"Facility" :
{ "Name" : "PureGym Piccadilly",
"Chain" : "PureGym" },
"SessionType" : "RunningProgram",
"DurationMins": 90,
"Exercises" :
[ { "Type" : "Leg press",
"ExCalories" : 28,
"Sets" :
[ { "Reps" : 14,
"Weight" : 60 },
. . .
] },
{ "Type" : "Tapis roulant" },
. . .
]
}
s1

17. Schema integration (example)
Enrico Gallinucci – University of Bologna SEBD 2020 17
Exercises_id
Type
ExCalories
_id
User.FullName
User.Age
StartedOn
Facility.Name
Facility.Chain
SessionType
DurationMins
Sets_id
Reps
Weight
Exercises_id
Type
ExCalories
_id
FirstName
LastName
Date
Gym.Name
Gym.City
Gym.Country
SessionType
DurationSecs
Series_id
ExType
Reps
Weight
SeriesCalories
_id
User.FullName
User.FirstName
User.LastName
User.Age
StartedOn
Facility.Name
Facility.Chain
Facility.City
Facility.Country
SessionType
DurationMins
Sets_id
Reps
Weight
SetCalories
WS
Sets
Exercises
WS
Series
WS
Sets
Exercises
s1 s2g

18. Schema integration
Goal: integrate the local schemas to obtain a single, global schema
• Schema matching: identify the single matches between the attributes
• Schema mapping: define mappings between each local schema and the global one
• Mappings include transcoding functions to enable query reformulation
• E.g., in the presence of selection predicates, or to integrate the query results
Schema integration is done in two steps
• Define a preliminary global schema as the
name-based union of all local schemas (automatic)
• Refine the preliminary global schema by merging
matching (sets of) fields (pay-as-you-go)
• Existing tools (e.g., Coma 3.0)can be used to
automatically find candidate matches
Enrico Gallinucci – University of Bologna SEBD 2020 18
Document store
Schema extraction
Schema integration
FD enrichment
Querying
Dependency graph
Global schema, Mappings
Local schemas