Vol-3194/paper75
Wolfgang Fahl
Paper
| Paper | |
|---|---|
 edit
| |
| description | |
| id | Vol-3194/paper75 |
| wikidataid | Q117345178→Q117345178 |
| title | Task-Driven Big Data Integration |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper75.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/Zecchini22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Paper[edit]
| Paper | |
|---|---|
| edit
| |
| description | |
| id | Vol-3194/paper75 |
| wikidataid | Q117345178→Q117345178 |
| title | Task-Driven Big Data Integration |
| pdfUrl | https://ceur-ws.org/Vol-3194/paper75.pdf |
| dblpUrl | https://dblp.org/rec/conf/sebd/Zecchini22 |
| volume | Vol-3194→Vol-3194 |
| session | → |
Task-Driven Big Data Integration[edit]
Task-Driven Big Data Integration
Luca Zecchini
Università degli Studi di Modena e Reggio Emilia, Modena, Italy
Abstract
Data integration aims at combining data acquired from different autonomous sources to provide the
user with a unified view of this data. One of the main challenges in data integration processes is entity
resolution, whose goal is to detect the different representations of the same real-world entity across the
sources, in order to produce a unique and consistent representation for it. The advent of big data has
challenged traditional data integration paradigms, making the offline batch approach to entity resolution
no longer suitable for several scenarios (e.g., when performing data exploration or dealing with datasets
that change with a high frequency). Therefore, it becomes of primary importance to produce new
solutions capable of operating effectively in such situations.
In this paper, I present some contributions made during the first half of my PhD program, mainly
focusing on the design of a framework to perform entity resolution in an on-demand fashion, building
on the results achieved by the progressive and query-driven approaches to this task. Moreover, I also
briefly describe two projects in which I took part as a member of my research group, touching on some
real-world applications of big data integration techniques, to conclude with some ideas on the future
directions of my research.
Keywords
Big Data Integration, Entity Resolution, Data Preparation
1. Introduction
Data integration aims at combining data acquired from different autonomous sources to provide
the user with a unified view of this data. The integration of different heterogeneous data sources
inherently poses several challenges: the sources can be organized according to different schemas,
they can contain duplicates, and so on. The advent of big data and the need to deal with its
features (the famous four Vs: volume, velocity, veracity, and variety) required a significant
evolution of data integration approaches and techniques, leading to the rise of new paradigms
designed to address this scenario.
Among the many challenges that data integration has to overcome, a central role is played
by Entity Resolution (ER), a.k.a. record linkage or deduplication, whose goal is to detect the
different representations of the same real-world entity across the sources (or even within the
same source), then to produce a unique and consistent representation for it. Performing ER
can be extremely challenging, due to the inconsistency of the different representations (e.g.,
compliance with different conventions, presence of missing and wrong values, etc.) and the
need to make the computational cost required to perform the comparisons between the pairs of
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ luca.zecchini@unimore.it (L. Zecchini)
� 0000-0002-4856-0838 (L. Zecchini)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
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α1() = MAJ.VOTING(<model>) GROUP BY ENTITY WITH MATCHER μ Matching function
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Batch ER
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exec. progressive emission clean results
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(a) Batch ER pipeline. (b) BrewER pipeline.
Figure 1: ER pipelines compared: batch ER vs BrewER.
records affordable (e.g., using a blocking function). Therefore, many algorithms and techniques
have been designed to address this problem [1, 2].
ER constitutes an essential step in the data preparation and cleaning pipeline [3], which is
required to ensure high data quality. An effective example for understanding the relevance of
data quality is that of data-driven decision making, which guides business decisions. Performing
data analysis on poor quality data would lead to potentially wrong results (garbage in, garbage
out), causing a negative economic impact for the business. The importance of data quality is
also increasingly highlighted in the field of artificial intelligence, where it is fundamental for the
effective training of machine learning models. In fact, even state-of-the-art models may fail to
perform well if the data used for their training is not properly prepared [4]. Therefore, increasing
relevance is given to the idea of moving from a model-centric to a data-centric approach1 .
In this paper, I present some contributions made during the first half of my PhD program.
In particular, in Section 2.1, I focus on BrewER, a framework to perform ER in an on-demand
fashion, which represents my main research contribution so far. Then, in Sections 2.2-2.3,
I describe two projects carried out by my research group (DBGroup2 ), which gave me the
opportunity to touch some real-world applications of big data integration and data preparation
techniques. Finally, in Section 3, I present some ideas on the future directions of my research.
2. Contributions
2.1. BrewER: Entity Resolution On-Demand
The well-established end-to-end batch approach to ER in big data scenario, mainly consisting of
blocking, matching, and fusion phases, requires to perform ER on the whole dataset in order
to run queries on the obtained clean version (Figure 1a). This is intuitively far from being an
efficient solution in several scenarios. For example, it is the case for data exploration, where the
data scientist is usually only interested in a certain portion of the dataset, and this interest can
be expressed through a query. In fact, the batch approach implies a lot of useless comparisons,
required to produce entities that are guaranteed not to appear in the result of the query. This
means wasting time, computational resources, and even money (e.g., pay-as-you-go cloud
1
https://datacentricai.org
2
https://dbgroup.unimore.it
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(a) Dirty camera dataset. (b) BrewER iterating on the priority queue.
Figure 2: BrewER in action.
computing). Such a situation can become critical when data changes with a high frequency
(e.g., when dealing with web data or in scenarios such as stock market trading) and there is
only a limited amount of time to detect the most relevant entities in the dataset according to
the interest expressed by the data scientist.
Progressive approaches presented in literature [5, 6, 7] aim at maximizing the number of
matches detected in a certain amount of time. However, these algorithms are guided by the
matching likelihood and do not allow the data scientist to define a priority on the entities.
Furthermore, in case of early stopping they would produce an approximate result, since they
proceed by considering the single candidate pairs of records and not the complete resolution of
the entities. On the other hand, the proposed query-driven solutions [8, 9] aim at cleaning only
the portion of dataset effectively useful to answer the query, but they still operate in a batch
manner, not supporting the progressive emission of the results.
BrewER (see [10] for a detailed explanation, [11] for a brief description of the intuition) is
designed to perform ER in an on-demand fashion, guided by the query expressing the interest
of the data scientist. BrewER is query-driven, since it performs ER only on the portion of the
dataset that might be useful for answering the query, according to its WHERE clauses (interpreted
in BrewER syntax as HAVING clauses, applied after performing a GROUP BY ENTITY step, as
depicted in Figure 1b), and progressive, since it returns the resulting entities (i.e., completely
resolved representative records) as soon as they are obtained, following the priority expressed
by the data scientist through the ORDER BY clause. To achieve this result, BrewER performs
a preliminary filtering of the blocks, keeping only the ones containing records (called seeds)
whose values might lead to the generation of an entity included in the result. Then, the records
appearing in the survived blocks are inserted in a priority queue, keeping for each one the
list of its candidate matches. The priority is defined according to the value of the attribute
appearing in the ORDER BY clause, in ascending or descending order. BrewER iterates on the
priority queue, considering at each iteration the head element: if it is a record, its candidate
matches are checked generating a completely resolved entity; otherwise (i.e., it is a completely
resolved entity), it is emitted or discarded based on whether or not it satisfies the query.
BrewER is agnostic towards the choice of the matching and blocking functions, and its
capability to perform clean queries on dirty data (as SQL select-project queries, with the
ORDER BY clause defining the emission priority) makes it a novel and powerful approach to ER.
�Figure 3: ECDP architecture.
2.2. ECDP: Energy Community Data Platform
ECDP (Energy Community Data Platform) [12] is a middleware platform designed to support
the collection and the analysis of big data about the energy consumption inside local energy
communities. Its goal is to promote a conscious use of energy by the users, paying particular
attention to self-consumption. The project saw the collaboration of the DBGroup and DataRiver,
respectively to design and to implement the platform, with the supervision of ENEA.
This project represents a concrete big data integration challenge, since the platform has to
acquire data of different nature (from the relational data about the users and the buildings to the
sensor data about the energy consumption/production or the weather conditions) from multiple
sources. The modular architecture of ECDP, depicted in Figure 3, was designed to promote
flexibility and scalability, meeting the needs of different types of users. In particular, ECDP
supports: (i) a data integration workflow, which exploits MOMIS [13, 14] for data integration
and a PostgreSQL RDBMS (optimized for time series using TimescaleDB and PostGIS) to store
the integrated and aggregated data, ready to be accessed by the standard users; (ii) a data lake
workflow, which relies on Delta Lake to store all raw data, allowing the advanced users to
perform some further analysis on it using Apache Spark.
2.3. DXP: Digital Experience Platform
DXP (Digital Experience Platform), subject of an ongoing project commissioned to the DBGroup
by Doxee, manages and processes billing data with the goal of providing services to the users (e.g.,
interactive billing) and analytics to the companies (e.g., churn prediction or user segmentation).
In the first part of this projects, aimed at performing data analysis on billing data, we had to
design a data preparation pipeline to get this data ready for the analysis. This gave me the
opportunity to deal with data preparation challenges in a real-world context, understanding its
fundamental impact on data analysis tasks.
�3. Future Directions
The topics covered during the first half of my PhD program offer many open challenges and
ideas on the future directions of my research, moving from (but not limited to) the path traced
by BrewER. In fact, BrewER itself presents many development possibilities. An evolution based
on the blocking graph, moving the priority from the record level to the block level, would make
it possible to support even unbounded aggregation functions (e.g., SUM); moreover, a specific
optimization for meta-blocking3 [15, 16, 17] would allow to consider in the algorithm also the
matching likelihood, further reducing the number of comparisons to be performed. Finally,
supporting join would represent a significant improvement, considering also the challenges
that need to be faced to adapt it to such a context.
Expanding the view from ER to the whole data preparation and cleaning pipeline, it is easy
to notice the presence of many different tasks [3]. Several tasks present specifically designed
solutions in the literature, but a lot of work still needs to be done towards an effectively usable
holistic tool. Data preparation presents many open challenges, and it is a relevant topic that
I aim to investigate, considering multi-task approaches [18] and possibly extending to other
tasks the on-demand approach that inspired BrewER.
Finally, operating with personal data (it is the case for medical data, but also for DXP)
raises privacy issues. Considering the impact on ER tasks, this implies further challenges to
overcome and calls into question privacy-preserving record linkage techniques [19]. The future
developments of DXP and new dedicated projects will allow me to delve into this topic, adapting
the existing solutions designed by the DBGroup to address these challenges and hopefully
adding new contributions to the research on this important aspect of big data management.
Acknowledgments
I wish to thank my tutor Prof. Sonia Bergamaschi and my co-tutor Giovanni Simonini, Prof.
Felix Naumann, and all the members of the DBGroup.
References
[1] V. Christophides, V. Efthymiou, T. Palpanas, G. Papadakis, K. Stefanidis, An Overview of
End-to-End Entity Resolution for Big Data, ACM Computing Surveys (CSUR) 53 (2021)
127:1–127:42.
[2] G. Papadakis, G. Mandilaras, L. Gagliardelli, et al., Three-dimensional Entity Resolution
with JedAI, Information Systems (IS) 93 (2020) 101565.
[3] M. Hameed, F. Naumann, Data Preparation: A Survey of Commercial Tools, ACM SIGMOD
Record 49 (2020) 18–29.
[4] L. Zecchini, G. Simonini, S. Bergamaschi, Entity Resolution on Camera Records without
Machine Learning, in: International Workshop on Challenges and Experiences from Data
Integration to Knowledge Graphs (DI2KG), volume 2726 of CEUR Workshop Proceedings,
CEUR-WS.org, 2020.
3
https://github.com/Gaglia88/sparker
� [5] S. E. Whang, D. Marmaros, H. Garcia-Molina, Pay-As-You-Go Entity Resolution, IEEE
Transactions on Knowledge and Data Engineering (TKDE) 25 (2013) 1111–1124.
[6] T. Papenbrock, A. Heise, F. Naumann, Progressive Duplicate Detection, IEEE Transactions
on Knowledge and Data Engineering (TKDE) 27 (2015) 1316–1329.
[7] G. Simonini, G. Papadakis, T. Palpanas, S. Bergamaschi, Schema-agnostic Progressive
Entity Resolution, in: IEEE International Conference on Data Engineering (ICDE), IEEE
Computer Society, 2018, pp. 53–64.
[8] H. Altwaijry, D. V. Kalashnikov, S. Mehrotra, Query-Driven Approach to Entity Resolution,
Proceedings of the VLDB Endowment (PVLDB) 6 (2013) 1846–1857.
[9] H. Altwaijry, S. Mehrotra, D. V. Kalashnikov, QuERy: A Framework for Integrating Entity
Resolution with Query Processing, Proceedings of the VLDB Endowment (PVLDB) 9
(2015) 120–131.
[10] G. Simonini, L. Zecchini, S. Bergamaschi, F. Naumann, Entity Resolution On-Demand,
Proceedings of the VLDB Endowment (PVLDB) 15 (2022) 1506–1518.
[11] L. Zecchini, Progressive Query-Driven Entity Resolution, in: International Conference on
Similarity Search and Applications (SISAP), volume 13058 of Lecture Notes in Computer
Science (LNCS), Springer, 2021, pp. 395–401.
[12] L. Gagliardelli, L. Zecchini, D. Beneventano, et al., ECDP: A Big Data Platform for the
Smart Monitoring of Local Energy Communities, in: International Workshop on Data Plat-
form Design, Management, and Optimization (DataPlat), volume 3135 of CEUR Workshop
Proceedings, CEUR-WS.org, 2022.
[13] S. Bergamaschi, S. Castano, M. Vincini, Semantic Integration of Semistructured and
Structured Data Sources, ACM SIGMOD Record 28 (1999) 54–59.
[14] S. Bergamaschi, D. Beneventano, F. Mandreoli, et al., From Data Integration to Big Data
Integration, in: A Comprehensive Guide Through the Italian Database Research Over the
Last 25 Years, volume 31 of Studies in Big Data, Springer, 2018, pp. 43–59.
[15] G. Simonini, S. Bergamaschi, H. V. Jagadish, BLAST: a Loosely Schema-aware Meta-
blocking Approach for Entity Resolution, Proceedings of the VLDB Endowment (PVLDB)
9 (2016) 1173–1184.
[16] L. Gagliardelli, G. Simonini, D. Beneventano, S. Bergamaschi, SparkER: Scaling Entity
Resolution in Spark, in: International Conference on Extending Database Technology
(EDBT), OpenProceedings.org, 2019, pp. 602–605.
[17] L. Gagliardelli, S. Zhu, G. Simonini, S. Bergamaschi, BigDedup: A Big Data Integration
Toolkit for Duplicate Detection in Industrial Scenarios, in: ISTE International Confer-
ence on Transdisciplinary Engineering (TE), volume 7 of Advances in Transdisciplinary
Engineering (ATDE), IOS Press, 2018, pp. 1015–1023.
[18] G. Simonini, H. Saccani, L. Gagliardelli, L. Zecchini, D. Beneventano, S. Bergamaschi, The
Case for Multi-task Active Learning Entity Resolution, in: Italian Symposium on Advanced
Database Systems (SEBD), volume 2994 of CEUR Workshop Proceedings, CEUR-WS.org,
2021, pp. 363–370.
[19] D. Vatsalan, Z. Sehili, P. Christen, E. Rahm, Privacy-Preserving Record Linkage for Big Data:
Current Approaches and Research Challenges, in: Handbook of Big Data Technologies,
Springer, 2017, pp. 851–895.
�
Task-Driven Big Data Integration[edit]
Task-Driven Big Data Integration
Luca Zecchini
Università degli Studi di Modena e Reggio Emilia, Modena, Italy
Abstract
Data integration aims at combining data acquired from different autonomous sources to provide the
user with a unified view of this data. One of the main challenges in data integration processes is entity
resolution, whose goal is to detect the different representations of the same real-world entity across the
sources, in order to produce a unique and consistent representation for it. The advent of big data has
challenged traditional data integration paradigms, making the offline batch approach to entity resolution
no longer suitable for several scenarios (e.g., when performing data exploration or dealing with datasets
that change with a high frequency). Therefore, it becomes of primary importance to produce new
solutions capable of operating effectively in such situations.
In this paper, I present some contributions made during the first half of my PhD program, mainly
focusing on the design of a framework to perform entity resolution in an on-demand fashion, building
on the results achieved by the progressive and query-driven approaches to this task. Moreover, I also
briefly describe two projects in which I took part as a member of my research group, touching on some
real-world applications of big data integration techniques, to conclude with some ideas on the future
directions of my research.
Keywords
Big Data Integration, Entity Resolution, Data Preparation
1. Introduction
Data integration aims at combining data acquired from different autonomous sources to provide
the user with a unified view of this data. The integration of different heterogeneous data sources
inherently poses several challenges: the sources can be organized according to different schemas,
they can contain duplicates, and so on. The advent of big data and the need to deal with its
features (the famous four Vs: volume, velocity, veracity, and variety) required a significant
evolution of data integration approaches and techniques, leading to the rise of new paradigms
designed to address this scenario.
Among the many challenges that data integration has to overcome, a central role is played
by Entity Resolution (ER), a.k.a. record linkage or deduplication, whose goal is to detect the
different representations of the same real-world entity across the sources (or even within the
same source), then to produce a unique and consistent representation for it. Performing ER
can be extremely challenging, due to the inconsistency of the different representations (e.g.,
compliance with different conventions, presence of missing and wrong values, etc.) and the
need to make the computational cost required to perform the comparisons between the pairs of
SEBD 2022: The 30th Italian Symposium on Advanced Database Systems, June 19-22, 2022, Tirrenia (PI), Italy
$ luca.zecchini@unimore.it (L. Zecchini)
� 0000-0002-4856-0838 (L. Zecchini)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
� Choose a matching function: μ()
- μ_customers_DL_Transfer_1 SELECT TOP 50 Q1
- μ_customers_DL_Transfer_2 model,mp,type,price c
... FROM products SELECT TOP 50 Q1
- μ_electronics_DL_custom_n WHERE mp > 10 VOTE(model), MAX(mp), Conflict resolution
... AND type LIKE `%slr%` functions
VOTE(type), MIN(price)
ORDER BY price DESC FROM products
Choose resolution functions:
α1() = MAJ.VOTING(<model>) GROUP BY ENTITY WITH MATCHER μ Matching function
α2() = MAX(<mp>) HAVING MAX(mp) > 10
α3() = MAJ.VOTING(<type>)
Q recall
new data AND VOTE(type) LIKE `%slr%`
Q recall
new data α4() = MIN(<price>) ORDER BY MIN(price) DESC
dirty data
dirty data
comparisons
comparisons
BrewER
Batch ER
Query
exec. progressive emission clean results
cleaned data clean results of cleaned data
(a) Batch ER pipeline. (b) BrewER pipeline.
Figure 1: ER pipelines compared: batch ER vs BrewER.
records affordable (e.g., using a blocking function). Therefore, many algorithms and techniques
have been designed to address this problem [1, 2].
ER constitutes an essential step in the data preparation and cleaning pipeline [3], which is
required to ensure high data quality. An effective example for understanding the relevance of
data quality is that of data-driven decision making, which guides business decisions. Performing
data analysis on poor quality data would lead to potentially wrong results (garbage in, garbage
out), causing a negative economic impact for the business. The importance of data quality is
also increasingly highlighted in the field of artificial intelligence, where it is fundamental for the
effective training of machine learning models. In fact, even state-of-the-art models may fail to
perform well if the data used for their training is not properly prepared [4]. Therefore, increasing
relevance is given to the idea of moving from a model-centric to a data-centric approach1 .
In this paper, I present some contributions made during the first half of my PhD program.
In particular, in Section 2.1, I focus on BrewER, a framework to perform ER in an on-demand
fashion, which represents my main research contribution so far. Then, in Sections 2.2-2.3,
I describe two projects carried out by my research group (DBGroup2 ), which gave me the
opportunity to touch some real-world applications of big data integration and data preparation
techniques. Finally, in Section 3, I present some ideas on the future directions of my research.
2. Contributions
2.1. BrewER: Entity Resolution On-Demand
The well-established end-to-end batch approach to ER in big data scenario, mainly consisting of
blocking, matching, and fusion phases, requires to perform ER on the whole dataset in order
to run queries on the obtained clean version (Figure 1a). This is intuitively far from being an
efficient solution in several scenarios. For example, it is the case for data exploration, where the
data scientist is usually only interested in a certain portion of the dataset, and this interest can
be expressed through a query. In fact, the batch approach implies a lot of useless comparisons,
required to produce entities that are guaranteed not to appear in the result of the query. This
means wasting time, computational resources, and even money (e.g., pay-as-you-go cloud
1
https://datacentricai.org
2
https://dbgroup.unimore.it
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Priority Queue
(a) Dirty camera dataset. (b) BrewER iterating on the priority queue.
Figure 2: BrewER in action.
computing). Such a situation can become critical when data changes with a high frequency
(e.g., when dealing with web data or in scenarios such as stock market trading) and there is
only a limited amount of time to detect the most relevant entities in the dataset according to
the interest expressed by the data scientist.
Progressive approaches presented in literature [5, 6, 7] aim at maximizing the number of
matches detected in a certain amount of time. However, these algorithms are guided by the
matching likelihood and do not allow the data scientist to define a priority on the entities.
Furthermore, in case of early stopping they would produce an approximate result, since they
proceed by considering the single candidate pairs of records and not the complete resolution of
the entities. On the other hand, the proposed query-driven solutions [8, 9] aim at cleaning only
the portion of dataset effectively useful to answer the query, but they still operate in a batch
manner, not supporting the progressive emission of the results.
BrewER (see [10] for a detailed explanation, [11] for a brief description of the intuition) is
designed to perform ER in an on-demand fashion, guided by the query expressing the interest
of the data scientist. BrewER is query-driven, since it performs ER only on the portion of the
dataset that might be useful for answering the query, according to its WHERE clauses (interpreted
in BrewER syntax as HAVING clauses, applied after performing a GROUP BY ENTITY step, as
depicted in Figure 1b), and progressive, since it returns the resulting entities (i.e., completely
resolved representative records) as soon as they are obtained, following the priority expressed
by the data scientist through the ORDER BY clause. To achieve this result, BrewER performs
a preliminary filtering of the blocks, keeping only the ones containing records (called seeds)
whose values might lead to the generation of an entity included in the result. Then, the records
appearing in the survived blocks are inserted in a priority queue, keeping for each one the
list of its candidate matches. The priority is defined according to the value of the attribute
appearing in the ORDER BY clause, in ascending or descending order. BrewER iterates on the
priority queue, considering at each iteration the head element: if it is a record, its candidate
matches are checked generating a completely resolved entity; otherwise (i.e., it is a completely
resolved entity), it is emitted or discarded based on whether or not it satisfies the query.
BrewER is agnostic towards the choice of the matching and blocking functions, and its
capability to perform clean queries on dirty data (as SQL select-project queries, with the
ORDER BY clause defining the emission priority) makes it a novel and powerful approach to ER.
�Figure 3: ECDP architecture.
2.2. ECDP: Energy Community Data Platform
ECDP (Energy Community Data Platform) [12] is a middleware platform designed to support
the collection and the analysis of big data about the energy consumption inside local energy
communities. Its goal is to promote a conscious use of energy by the users, paying particular
attention to self-consumption. The project saw the collaboration of the DBGroup and DataRiver,
respectively to design and to implement the platform, with the supervision of ENEA.
This project represents a concrete big data integration challenge, since the platform has to
acquire data of different nature (from the relational data about the users and the buildings to the
sensor data about the energy consumption/production or the weather conditions) from multiple
sources. The modular architecture of ECDP, depicted in Figure 3, was designed to promote
flexibility and scalability, meeting the needs of different types of users. In particular, ECDP
supports: (i) a data integration workflow, which exploits MOMIS [13, 14] for data integration
and a PostgreSQL RDBMS (optimized for time series using TimescaleDB and PostGIS) to store
the integrated and aggregated data, ready to be accessed by the standard users; (ii) a data lake
workflow, which relies on Delta Lake to store all raw data, allowing the advanced users to
perform some further analysis on it using Apache Spark.
2.3. DXP: Digital Experience Platform
DXP (Digital Experience Platform), subject of an ongoing project commissioned to the DBGroup
by Doxee, manages and processes billing data with the goal of providing services to the users (e.g.,
interactive billing) and analytics to the companies (e.g., churn prediction or user segmentation).
In the first part of this projects, aimed at performing data analysis on billing data, we had to
design a data preparation pipeline to get this data ready for the analysis. This gave me the
opportunity to deal with data preparation challenges in a real-world context, understanding its
fundamental impact on data analysis tasks.
�3. Future Directions
The topics covered during the first half of my PhD program offer many open challenges and
ideas on the future directions of my research, moving from (but not limited to) the path traced
by BrewER. In fact, BrewER itself presents many development possibilities. An evolution based
on the blocking graph, moving the priority from the record level to the block level, would make
it possible to support even unbounded aggregation functions (e.g., SUM); moreover, a specific
optimization for meta-blocking3 [15, 16, 17] would allow to consider in the algorithm also the
matching likelihood, further reducing the number of comparisons to be performed. Finally,
supporting join would represent a significant improvement, considering also the challenges
that need to be faced to adapt it to such a context.
Expanding the view from ER to the whole data preparation and cleaning pipeline, it is easy
to notice the presence of many different tasks [3]. Several tasks present specifically designed
solutions in the literature, but a lot of work still needs to be done towards an effectively usable
holistic tool. Data preparation presents many open challenges, and it is a relevant topic that
I aim to investigate, considering multi-task approaches [18] and possibly extending to other
tasks the on-demand approach that inspired BrewER.
Finally, operating with personal data (it is the case for medical data, but also for DXP)
raises privacy issues. Considering the impact on ER tasks, this implies further challenges to
overcome and calls into question privacy-preserving record linkage techniques [19]. The future
developments of DXP and new dedicated projects will allow me to delve into this topic, adapting
the existing solutions designed by the DBGroup to address these challenges and hopefully
adding new contributions to the research on this important aspect of big data management.
Acknowledgments
I wish to thank my tutor Prof. Sonia Bergamaschi and my co-tutor Giovanni Simonini, Prof.
Felix Naumann, and all the members of the DBGroup.
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3
https://github.com/Gaglia88/sparker
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