=Paper=
{{Paper
|id=Vol-2738/paper18
|storemode=property
|title=Towards Evolutionary, Domain-Specific Query Classification Based on Policy Rules
|pdfUrl=https://ceur-ws.org/Vol-2738/LWDA2020_paper_18.pdf
|volume=Vol-2738
|authors=Peter K. Schwab,Klaus Meyer-Wegener
|dblpUrl=https://dblp.org/rec/conf/lwa/SchwabM20
}}
==Towards Evolutionary, Domain-Specific Query Classification Based on Policy Rules==
https://ceur-ws.org/Vol-2738/LWDA2020_paper_18.pdf
Towards Evolutionary, Domain-Specific Query
Classification Based on Policy Rules
Peter K. Schwab and Klaus Meyer-Wegener
Friedrich-Alexander-Universität Erlangen-Nürnberg
{firstname.lastname}@fau.de, https://www.cs6.tf.fau.eu/
Abstract. Many devices like smart sensors produce a vast amount of
data that are still commonly stored in relational databases and are being
processed using SQL queries. This data is only useable if it is processed
in a fashion that results in applicable information for the users posing
these queries. Thus, it can be very supportive for them to assess other
queries that have already processed the targeted data. This is not a
simple exercise, as SQL allows alias names and various syntactic struc-
tures to express equivalent queries. A manual assessment is also hard to
accomplish due to the amount of qualified queries. We present a frame-
work for evolutionary SQL query classification. Based on the analysis of
query logs, query metadata like schema lineage and result statistics are
automatically derived. Our framework enables users to define domain-
specific policy rules for automatic query classification based on the query
metadata. Classification is done according to domain-specific, contextual
attributes that can be defined evolutionary at runtime, together with the
policy rules. The classification results enrich the query metadata.
1 Introduction
Hoarding vast amounts of data is no longer a big thing to undertake, for example
by smart sensors in the context of Industry 4.0. Instead, the key task is to extract
the desired information from the data for a particular purpose at a certain point
in time [3]. Data is only useable when accessed in a fashion that results in appli-
cable information for the users posing the accessing queries. Thus, it can be very
supportive for them to assess other queries that already have accessed the tar-
geted data set. Most data are still commonly stored in relational databases (DBs)
and are being processed using SQL queries. Analyzing these queries regarding
their kind of data access is not a simple exercise, as SQL allows alias names
and various syntactic structures for equivalent queries (e. g. subquery instead of
join). Query assessment can be supported by considering query metadata (QM).
A manual assessment is often hard to accomplish because of the vast amount of
qualified queries. In addition, most query-assessment results are not commonly
accessible but only available as tacit knowledge in the heads of the resp. users.
Copyright ⃝c 2020 by the paper’s authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).
Problem Statement. We require novel approaches that support query assess-
ment according to a certain processing context. They must ease the analysis of
the SQL queries’ syntactical variety and support automation of the query classi-
fication. Users must be enabled to store their assessment results linked with the
underlying queries in order to share them with other users.
Contribution. We present a framework for evolutionary, domain-specific SQL
query classification based on policy rules. QM like schema lineage and result
statistics are automatically derived based on the analysis of query logs. Our
framework enables users to externalize their tacit knowledge into domain-specific
policy rules for automatic query classification based on the QM. Classification
results are stored in contextual QM attributes (QMAs). They can be used for
further classification in other policy rules. The contextual QMAs as well as the
policy rules can be defined evolutionary at runtime.
2 Policy-Based Query Classification
We provide a policy-based, automatic query classification [8] based on relational
and graph-based data models holding QM [7].
Evolutionary Definition of Contextual Query Metadata. Examples for
contextual QM are a query’s purpose, its compliance according to data-privacy
directives, or its aptitude for hardware acceleration. So far, this type of QM was
mapped to our relational model. We extend our multi-relational property-graph
model [7] to enable the evolutionary definition of contextual QM at runtime.
For every query, a graph with a root vertex holding a UID is modeled. This root
can have several edges of type hasContextualAttr to vertices of type contextualAt-
tibute. A vertex has two properties holding the name and the value of the resp.
contextual QMA. In addition, a contextualAttibute vertex has exactly one edge
hasDataType to a vertex of type dataType. To support a slender, user-centric set
of data types that can be selected for contextual QMA, we orientate towards the
requirements interchange format (ReqIF) as a standard for user-centric types
that fulfill an end-user’s plain idea of data types [2] and provide Boolean, String,
Integer, Float, Timestamp, and Enumeration, and lists of these data types.
Domain-Specific Policy Rules. Our policies are based on conditional rules.
The Boolean expression in their antecedent part describes a query-processing
pattern. We provide a domain-specific language to write it down [8]. Our query
representation is independent from SQL syntax using the queries’ corresponding
trees of relational algebra operators. A single tree covers many syntactical vari-
ants of semantically equivalent queries. This syntax-independent representation
enables a more generic definition of patterns. A basic pattern pbasic is related to
a single QM attribute and covers for example an accessed relation or schema
attribute, a certain filter predicate, the related DB user, or a query’s runtime or
number of result tuples. Schema lineage is resolved automatically. Up to now,
basic patterns have been combined by logical conjunction to a complex pattern
pcomplex . We extend the combination possibilities by adding Boolean operators
(NOT, OR, XOR) and nesting via parentheses to create richer complex patterns.
Queries matching a complex pattern will be classified according to the rule’s
consequent part. So far, this part was fixed on the contextual QMA data-privacy
compliance. Based on our data-privacy use case [6], any policy rule classified a
matching query q always as non-compliant (cf. List. 1, line 2).
We extend our policy rules’ consequent part and allow classification to ar-
bitrary contextual QMAs. Now both contextual QMAs and policy rules can be
defined at runtime. The assigned value v has to match the data domain of the
related contextual QMA qmac (cf. List. 1, line 5). When q is classified, its con-
textual QM is enriched with the classification result and the related policy IDs
of all matching rules. This enables traceability of the classification process.
1 /* Status Quo of Policy - Rule Definition */
2 IF q . match ( p complex ) THEN q . classify ( ‘non - compliant ’ )
3
4 /* Evolutionary Policy Rules */
5 IF q . match ( p complex ) THEN q . classify ( qma c , v )
Listing 1. Status quo of our policy rules and the proposed extension.
3 Exemplary Classification Use Case
Up to now, our approach was tailored towards the use case of data-privacy
compliance [6,7,8]. Examples for policy rules in this context are the prohibition
of filters on certain personal data or the requirement of a minimum result size
in order to prevent users from drawing conclusions on individuals by queries.
We will motivate now another use case that is totally different to the present
one in order to demonstrate that by enabling evolutionary contextual QMAs,
policy-based query classification can be applied in arbitrary scenarios without
the need of adapting our implementation.
To enable hardware-based acceleration of DB query processing, the project
“Reconfigurable Data Provider (ReProVide)” provides a sophisticated storage
solution based on field-programmable gate arrays (FPGAs) [1]. Its query opti-
mization techniques consider the capabilities of the hardware for a scalable and
highly performant near-data processing of Big Data [5]. ReProVide’s generic
FPGA architecture offers a library of query-processing modules, which can be
configured onto the FPGAs. So far, queries apt for near-data processing are se-
lected manually based on tacit expert knowledge. The ReProVide system is a
system on a chip (SoC) with its own storage [4]. Only queries accessing data
that are stored there can be accelerated by the FPGAs. Filter operators, for ex-
ample, can be accelerated at line rate. But there are different query-processing
modules for filters, depending on the involved data type. Thus, assuming that
the date dimension of the TPC-DS benchmark suite1 is located on the RePro-
1
http://www.tpc.org/tpcds/
Vide storage, a responsible DB administrator could first create a new contex-
tual QMA at runtime with name hardware-acceleration aptitude and data type
List with the elements {’filter (float)’, ’filter (int)’, ’filter (uint)’,
’filter (boolean)’, ’filter (string)’, ’filter (date)’, ’filter (timestamp)’}. Then, the
admin could create the policy rule shown in List. 2 which triggers automatic
classification of queries containing filter operations on integers. The admin ac-
cordingly creates further policy rules covering filter operations on other data
types. This means, a query containing several filter operations on different data
types can be classified by different policy rules. Therefore, our contextual QMA
was defined as a List. For example, the query in List. 3 will finally be classified
as hardware-acceleration aptitude = {‘filter (int)’, ‘filter (string)’}.
1 IF q . match (
2 restrictsOn ( ‘ date_dim ’ , ‘ d_year ’) OR
3 restrictsOn ( ‘ date_dim ’ , ‘ d_dow ’) OR
4 ...
5 restrictsOn ( ‘ date_dim ’ , ‘ d_last_dom ’)
6 )
7 THEN q . classify ( ‘ hardware - acceleration aptitude ’ ,
8 ‘ filter ( int ) ’
9 )
Listing 2. Example rule to classify queries apt for hardware acceleration.
1 SELECT d_year , d_dow
2 FROM date_dim
3 WHERE d_day_name = " Monday " AND ( d_year > 1900 OR d_moy > 4)
Listing 3. Example query that filters on data stored within the ReProVide system.
As ReProVide also allows hardware acceleration of projections and semi-
joins, the enumeration’s elements of our contextual QMA can be extended by
respective elements and new policy rules could be defined to enable classification
of queries containing these operations. All of this can happen at runtime, without
adapting our implementation. Furthermore, the authors of ReProVide also aim
query-sequence optimization [4]. Our framework can also support this aim by
defining additional contextual QMAs and policy rules – again at runtime.
4 Next Steps
We will elaborate the use case for hardware acceleration in more detail concern-
ing our approach. Furthermore, we have to solve the problem of contradicting
classification results based on conflicting policy rules. A prototypic implemen-
tation will give further information about the applicability of our evolutionary
approach for arbitrary scenarios.
Acknowledgement: The authors would like to thank the anonymous reviewers for their
valuable remarks.
References
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architectures for near-memory data processing. In: BTW, 18. Fachtagung des GI-
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