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+ | + Posters Session + | +
| Automated Multidimensional Data Layouts in Amazon Redshift
+ Parchas, Panos* + Click to display the abstract + + "Analytic data systems typically use data layouts to improve the performance of scanning and filtering data. Common data layout techniques + include single-column sort keys, compound sort keys (also known as composite sort keys), and more complex multidimensional data layouts such as + the Z-order. An appropriately-selected data layout over a table, in combination with metadata such as zone maps, enable the system to skip + irrelevant data blocks when scanning the table, which reduces the amount of data scanned and improves query performance. + + In this paper, we introduce Multidimensional Data Layouts (MDDL), a new data layout technique which outperforms existing data layout + techniques for query workloads with repetitive scan filters. Unlike existing data layout approaches, which typically sort tables + based on columns, MDDL sorts tables based on a collection of predicates, which enables a much higher degree of specialization to + the user's workload. We additionally introduce an algorithm for automatically learning the best MDDL for each table based on + telemetry collected from the historical workload. We implemented MDDL within Amazon Redshift. Benchmarks on internal datasets + and workloads show that MDDL achieves up to 85% reduction in end-to-end workload runtime and up to 100X faster performance on + individual queries, compared to using traditional column-based data layout techniques. MDDL is, to the best of our knowledge, + the first data layout technique in a commercial product that sorts based on predicates and automatically learns the best predicates. + MDDL has now been released to customers in Public Preview." + + |
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+ XDB in Action: Decentralized Cross-Database Query Processing for Black-Box DBMSes
+ Haralampos Gavriilidis (Technische Universität Berlin)*; Leonhard Rose (Technische Universität Berlin); Joel Ziegler (Technische Universität Berlin); + Kaustubh Beedkar (IIT Delhi); Jorge Arnulfo Quiané Ruiz (IT University of Copenhagen); Volker Markl (Technische Universität Berlin) + + Click to display the abstract + + Data are naturally produced at different locations and hence stored on different DBMSes. To maximize the value of the collected data, + today's users combine data from different sources. Research in data integration has proposed the Mediator-Wrapper (MW) architecture to + enable ad-hoc querying processing over multiple sources. The MW approach is desirable for users, as they do not need to deal with heterogeneous + data sources. However, from a query processing perspective, the MW approach is inefficient: First, one needs to provision the mediating execution + engine with resources. Second, during query processing, data gets "centralized" within the mediating engine, which causes redundant data movement. + Recently, we proposed in-situ cross-database query processing, a paradigm for federated query processing without a mediating engine. Our approach optimizes + runtime performance and reduces data movement by leveraging existing systems, eliminating the need for an additional federated query engine. In this demonstration, + we showcase XDB, our prototype for in-situ cross-database query processing. We demonstrate several aspects of XDB, i.e. the cross-database environment, + our optimization techniques, and its decentralized execution phase. + + |
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+ Perseus: Verifiable Databases Beyond Merkle Trees
+ Charalampos Papamanthou (Yale University)* + + Click to display the abstract + + We propose Perseus, a system for cryptographically verifying SQL queries. In Perseus, relational tables are represented with succinct digests. + All SQL queries are decomposed into SQL operators for which we provide custom MAP/REDUCE protocols based on recursive succinct proof systems. + The Perseus prover is distributed (both across operators and within each operator) and produces proofs sublinear in size. + Our preliminary experimental estimates on the well-known TPC-H benchmark used by the database community show that Perseus outperforms + the baseline by 139x and 6.11x on proof size and updates, respectively. + + |
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+ DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines
+ Aristotelis Vontzalidis (Computing Systems Lab - National Technical University of Athens); + Constantinos Bitsakos (NTUA); Stratos Psomadakis (Computing Systems Lab - National Technical University of Athens); + Mark Dokter (Know-Center GmbH); Kevin Innerebner (Graz University of Technology); Patrick Damme (Technische Universität Berlin); + Matthias Boehm (Technische Universität Berlin); Florina M. Ciorba (Technical University of Dresden, Germany / University of Basel, Switzerland); + Ahmed Eleliemy (University of Basel); Vasileios Karakostas (National and Kapodistrian University of Athens); Aleš Zamuda (University of Maribor); + Dimitrios Tsoumakos (National Technical University of Athens)* + + Click to display the abstract + + Integrated data analysis pipelines combine rigorous data management and processing, high-performance computing and machine learning tasks. + While these systems and operations share many compilation and runtime techniques, data analysts and scientists are currently dealing with + multiple systems for each stage of their pipeline. DAPHNE is an open and extensible system infrastructure for such pipelines, including + language abstractions, compilation and runtime techniques, multi-level scheduling, hardware accelerators and computational storage. + In this demonstration, we focus on the DAPHNE runtime that provides the implementation of kernels for local, distributed and + accelerator-enhanced operations, vectorized execution, integration with existing frameworks and libraries for productivity and + interoperability, as well as efficient I/O and communication primitives. + + |
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+ Assess Queries for Interactive Analysis of Data Cubes
+ Panos Vassiliadis (University of Ioannina)* + + Click to display the abstract + + Assessment is the process of comparing the actual to the expected + behavior of a business phenomenon and judging the outcome of + the comparison. In this paper we propose assess, a novel querying + operator that supports assessment based on the results of a + query on a data cube. This operator requires (1) the specification + of an OLAP query over a measure of a data cube, to define the + target cube to be assessed; (2) the specification of a reference cube + of comparison (benchmark), which represents the expected performance + of the measure; (3) the specification of how to perform + the comparison between the target cube and the benchmark, and + (4) a labeling function that classifies the result of this comparison + using a set of labels. After introducing an SQL-like syntax for + our operator, we formally define its semantics in terms of a set + of logical operators. To support the computation of assess we + propose a basic plan as well as some optimization strategies, then + we experimentally evaluate their performance using a prototype. + + |
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+ TrajParquet: A Trajectory-Oriented Column File Format for Mobility Data Lakes
+ Nikolaos Koutroumanis (University of Piraeus)*; Christos Doulkeridis (University of Pireaus); + Chiara Renso (ISTI-CNR); Mirco Nanni (CNR-ISTI Pisa, Italy); Raffaele Perego (ISTI-CNR) + + Click to display the abstract + + Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage + and querying data lakes, due to compressed storage and efficient data access via data skipping. However, + when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over + single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, + such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data + (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage + of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that + supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets + and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data. + + |
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+ Unanimous 2PC: Fault-tolerant Distributed Transactions Can be Fast and Simple
+ Antonios Katsarakis (Huawei Research)* + + Click to display the abstract + + Distributed transactional datastores are pivotal in supporting the needs of modern applications and services. + Datastores rely on distributed transactional protocols to tolerate faults while also aiming for strong consistency + and high performance. State-of-the-art distributed transactional protocols, such as FaRM, provide a lock-free execution phase, + thus improving performance and simplifying recovery. However, these protocols often come with lengthy and complicated commit phases, + requiring multiple round-trips to commit a transaction (or additional replicas). This completely contrasts with the simplicity + and efficiency of the traditional two-phase commit (2PC) protocol, which can commit a transaction after a single round-trip, + albeit lacking fault tolerance. + + To address the limitations of both approaches, we introduce U2PC, a novel 2PC variant that combines simplicity, efficiency, + and fault tolerance. U2PC frugally replicates data (f+1) times to tolerate up to f faults with strong consistency. + It offers a single round-trip commit after unanimous responses of all replicas of the involved shards and safe recovery + via an extra ""pre-abort"" round before aborting a transaction. Our verification in TLA+ confirms that U2PC achieves strict + serializability and recovers safely. In short, U2PC ensures fault tolerance, optimizes performance for common scenarios, + and maintains uniform transaction handling. + + |
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+ CAP Off: Local Reads and Linearizable Asynchronous Replication
+ Antonios Katsarakis (Huawei Research)* + + Click to display the abstract + + Distributed datastores are the backbone of highly concurrent read-intensive applications and are responsible for providing consistency, + availability, and performance. Datastores deploy crash-tolerant protocols to replicate data across servers and endure replica server crashes. + To ensure safety and accommodate the performance demands of datastores, replication protocols must offer high throughput reads that are + strongly consistent (i.e., linearizable) without making any synchrony assumptions. However, existing crash-tolerant protocols either (1) + relax consistency; or offer linearizable reads that are either (2) fully asynchronous and remote (i.e., involve multiple replicas); + or (3) local but mandate some sort of synchrony. + + This work reveals a fundamental tradeoff between consistency, asynchrony, and the performance of crash-tolerant protocols by + introducing the L2AW impossibility. The L2AW asserts that: in any linearizable asynchronous read/write register implementation that + tolerates a single crash, no reads are local. We prove this impossibility without strong assumptions -- i.e., in the absence of + Byzantine faults, message losses, and network partitions. + + Guided by this result, we introduce almost-local reads (ALRs), which can be implemented in a crash-tolerant and linearizable + manner under asynchrony. ALRs inevitably have higher latency than local (single-node) reads, but they are very lightweight, + with computation and network costs close to local reads. As such, we demonstrate that the tradeoff exposed by LAW affects mainly + the latency and not the throughput of reads. We present two simple yet effective ALR schemes that can improve protocols falling + into either of the three categories. When applied to protocols offering local reads, they remove their consistency or synchrony + deficiencies with minimal (2-5% throughput) overhead. ALRs can also boost the performance (2.5x higher throughput) of existing + asynchronous linearizable protocols without compromises. + + |
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+ Dandelion: Hundreds of Millions of Distributed Replicated Transactions with Few Machines
+ Antonios Katsarakis (Huawei Research)*; Vasilis Gavrielatos (Huawei); Nikos Ntarmos (Edinburgh Research Center, Central Software Institute, Huawei) + + Click to display the abstract + + We present an in-memory, RDMA-enabled, highly-available, transactional Key-Value Store (KVS), dubbed Dandelion, focusing on significantly + improving performance in small deployments (e.g., 5 machines). The focus on small deployments is motivated by the emerging memory expansion + (e.g., via CXL), which enables the deployment of KVSes with few machines but lots of memory. + + A small deployment presents locality opportunities that have not been examined by related work. Specifically, it is more likely that at any + given time, we must send multiple messages to the same recipient. We leverage this by batching multiple requests in the same network packet. + Similarly, it is more likely that at any given time, we have multiple requests that can be served by the local hashtable without going through + the network. Sending all requests to the hashtable as a batch allows it to overlap their memory latencies through software prefetching. + Finally, it is more likely that the node that requests a key, is itself a backup of that key. We leverage this by allowing local reads from backups. + + Our evaluation shows that these optimizations and careful engineering results in hundreds of millions of distributed, + strongly consistent, and 3-way replicated transactions per second with very few machines. This also translates to 3.3 - 6.5x throughput + improvement over a state-of-the-art system, FaSST, in OLTP workloads in a 5-machine deployment. We characterize the impact and scalability + of each of these optimizations with up to 10 machines – where Dandelion still offers up to 3.5× higher throughput than FaSST." + + |
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+ On Explaining Unfairness: An Overview
+ Christos Fragkathoulas (University of Ioannina;Athena Research Center)*; Vasiliki Papanikou (University of Ioannina; Athena Research Center); + Danae Pla Karidi (Archimedes, Athena RC); Evaggelia Pitoura (Univ. of Ioannina) + + Click to display the abstract + + Algorithmic fairness and explainability are foundational elements for achieving responsible AI. In this paper, we focus on their interplay, + a research area that is recently receiving increasing attention. To this end, we first present two comprehensive taxonomies, + each representing one of the two complementary fields of study: fairness and explanations. Then, we categorize explanations for + fairness into three types: (a) Explanations to enhance fairness metrics, (b) Explanations to help us understand the causes of (un)fairness, + and (c) Explanations to assist us in designing methods for mitigating unfairness. Finally, based on our fairness and explanation taxonomies, + we present undiscovered literature paths revealing gaps that can serve as valuable insights for future research. + + |
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+ TokenJoin: Efficient Filtering for Set Similarity Join with Maximum Weighted Bipartite Matching
+ Alexandros Zeakis (National and Kapodistrian University of Athens)*; Dimitrios Skoutas (Athena Research Center); + Dimitris Sacharidis (ULB); Odysseas Papapetrou (TU Eindhoven); Manolis Koubarakis (University of Athens, Greece) + + Click to display the abstract + + Set similarity join is an important problem with many applications in data discovery, cleaning and integration. To increase robustness, + fuzzy set similarity join calculates the similarity of two sets based on maximum weighted bipartite matching instead of set overlap. + This allows pairs of elements, represented as sets or strings, to also match approximately rather than exactly, e.g., based on Jaccard + similarity or edit distance. However, this significantly increases the verification cost, making even more important the need for efficient + and effective filtering techniques to reduce the number of candidate pairs. The current state-of-the-art algorithm relies on similarity + computations between pairs of elements to filter candidates. In this paper, we propose token-based instead of element-based filtering, + showing that it is significantly more lightweight, while offering similar or even better pruning effectiveness. Moreover, we address + the top-k variant of the problem, alleviating the need for a user-specified similarity threshold. We also propose early termination + to reduce the cost of verification. Our experimental results on six real-world datasets show that our approach always outperforms + the state of the art, being an order of magnitude faster on average. + + |
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+ QFusor: A UDF Optimizer Plugin for SQL Databases
+ Konstantinos Chasialis (Athena RC); Theoni Palaiologou (University of Athens); Yannis Foufoulas (Athena Research Center)*; + Alkis Simitsis (Athena Research Center); Yannis Ioannidis (University of Athens) + + Click to display the abstract + + Modern data applications in areas such as text + mining, document analysis, and data science, involve complex + algorithms and logic that cannot be expressed in SQL. Therefore, + SQL databases employ user-defined functions (UDFs) to extend + their supported functionality. However, this comes at a significant + performance cost as UDFs routinely become the bottleneck in + query execution. To deal with this problem, we present QFusor, + an optimizer plugin for UDF queries in relational databases. + QFusor minimizes the performance overheads introduced by + the impedance mismatch between the UDF and SQL execution + environments by employing techniques such as vectorization, + parallelization, tracing JIT compilation, and operator fusion + for various types of UDF (scalar, aggregate, table UDFs) and + relational operators. QFusor follows a pluggable, engine-agnostic + design and can work with several popular SQL databases offering + a significant boost in their UDF query performance. + + |
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+ MIP: Advanced Data Processing and Analytics for Science and Medicine
+ Kostas Filippopolitis (Athena Research Center); Yannis Foufoulas (Athena Research Center)*; Minos Garofalakis (ATHENA Research Center); + Apostolos Glenis (Athena Research Center); Yannis Ioannidis (University of Athens); Thanasis-Michail Karampatsis (ATHENA R.C.); + Maria - Olympia Katsouli (Athena Research Center); Evdokia Mailli (ATHENA RC); Asimakis Papageorgiou-Mariglis (Athena Research Center); + Giorgos Papanikos (Athena Research Center); George Pikramenos (Athena Research Center); Jason Sakellariou (Athena Research Center); + Alkis Simitsis (Athena Research Center); Pauline Ducouret (CHUV); Philippe Ryvlin (CHUV); Manuel-Guy Spuhler (CHUV) + + Click to display the abstract + + We present the Medical Informatics Platform (MIP), an online col- laborative platform for the scientific and medical community. + It federates de-centralized patient data located in hospitals, helping clinicians, clinical scientists, and researchers identify + patterns unique to diseases and provide clear diagnoses and personalized treatments. The platform enables users to access + harmonized medical data from pre-processed neurophysiological and med- ical records, and research cohort datasets without + the need to transfer original clinical data. This functionality facilitates explo- ration and analysis of medical data while + preserving the privacy and security of sensitive patient information. The MIP blends data science and machine learning with + data technology, and especially data integration, secure computation, decentralized distributed query execution, and low level, + efficient execution of science pipelines exploiting features of modern data engines such as vectorization, parallelization, + and JIT compilation. The MIP is the result of a multi-disciplinary, multi-year effort among computer scientists, + clinical scientists and medical professionals. To date, it has been deployed and used in 40+ hospitals across Europe and + another 12 installations are in process. + + + |
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+ Apollo+: A Dataset Profiling and Operator Modeling System for Distributed, Dynamic Datasets
+ Constantinos Pefkianakis (Database and Knowledge Systems Lab, National Technical University of Athens); + Ioannis Giannakopoulos (Computing Systems Lab, NTUA); Dimitrios Tsoumakos (National Technical University of Athens)* + + Click to display the abstract + + Apollo showcased a dataset profiling methodology that + can infer the output of an analytics operator executed on + different input datasets. By quantifying dataset similarities + based on statistical distribution, size, and data ordering, Apollo + can model and predict the output of a wide range of analytics + operators over both tabular and graph datasets. This demonstration presents Apollo+, a scalable system for modeling + analytics operator behavior over dynamic and distributed + datasets. It incrementally maintains dataset similarity projections as new data arrives through microbatch or streaming + updates. By adaptively recomputing only affected similarities, Apollo+ enables low-overhead operator output approximation for evolving data. It also takes into consideration the + location of the datasets considering data locality and transfer + costs. + + |
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+ Θυμόμαστε τα Ξεχασμένα: Ομαδοποίηση, Ανίχνευση Ανωμαλιών και Προσαρμογή Ακρίβειας σε μια Διαδικασία Αναπλήρωσης Δεδομένων
+ "Anna Baskin (University of Pittsburgh); Scott Heyman (University of Pittsburgh ); Brian Nixon (University of Pittsburgh); + Constantinos Costa (Rinnoco Ltd)*; Panos K. Chrysanthis (University of Pittsburgh)" + + Click to display the abstract + + Η συνεχώς αυξανόμενη ζήτηση για χρήση και αποθήκευση δεδομένων επ' αόριστον περιορίζεται από το κόστος αποθήκευσης, το οποίο μειώνεται αργά σε σύγκριση με την + εκθετική αύξηση της υπολογιστικής ισχύος. Υπό αυτές τις συνθήκες, η σκόπιμη απώλεια λεπτομερειών στα δεδομένα καθώς αυτά παλαιώνουν (αναφερόμενη ως φθορά δεδομένων) + είναι χρήσιμη επειδή επιτρέπει τη μείωση του κόστους αποθήκευσης παράλληλα με τη χρησιμότητα των δεδομένων. Η ιδέα της αναπλήρωσης δεδομένων ως μέθοδος φθοράς + δεδομένων χρησιμοποιεί τεχνικές μηχανικής μάθησης για την ανάκτηση προηγουμένως διαγραμμένων τιμών από την αποθήκευση δεδομένων. + + Αυτή η εργασία προτείνει και αξιολογεί μια νέα διαδικασία χρησιμοποιώντας ομαδοποίηση, ανίχνευση ανωμαλιών, μηχανική μάθηση και προσαρμογή ακρίβειας για + την υλοποίηση μιας αποτελεσματικής αναπλήρωσης δεδομένων για την αρχειοθέτηση δεδομένων. Συνολικά, ο στόχος είναι η εκπαίδευση ενός μοντέλου μηχανικής + μάθησης για την προσέγγιση των τιμών μιας βάσης δεδομένων, επιτρέποντας τη διαγραφή ολόκληρων στηλών, οι οποίες μπορούν αργότερα να ανακατασκευαστούν + εντός ενός ορίου ακρίβειας χρησιμοποιώντας τα αποθηκευμένα μοντέλα. + + Αξιολογήσαμε την αποτελεσματικότητα της διαδικασίας αναπλήρωσης δεδομένων όσον αφορά τη μείωση της αποθήκευσης και την ακρίβεια ανάκτησης δεδομένων + χρησιμοποιώντας ένα πραγματικό σύνολο δεδομένων υγειονομικής περίθαλψης. Τα πρώτα μας αποτελέσματα δείχνουν ότι η σειρά με την οποία εφαρμόζονται οι + μέθοδοι ανίχνευσης ανωμαλιών, ομαδοποίησης και μηχανικής μάθησης οδηγεί σε διαφορετικές συμβιβαστικές λύσεις όσον αφορά την αποθήκευση και την ακρίβεια ανάκτησης. + + |
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+ Big Data Compression Tool using Attribute-based Signatures
+ Constantinos Costa (Rinnoco Ltd)*; Panos K. Chrysanthis (Rinnoco Ltd); Herodotos Herodotou (Cyprus University of Technology); + Marios Costa (Rinnoco Ltd); Efstathios Stavrakis (Algolysis); Nicolas Nicolaou (Algolysis) + + Click to display the abstract + + This paper introduces COMPASS, a multiple compression tool utilizing attribute-based signatures. COMPASS exploits K-means clustering to select the best + compression scheme for different data subsets in a database. The experimental results show that COMPASS significantly reduces disk space usage compared + to monolithic methods. + + |
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+ EcoCharge: Ένα Πλαίσιο Διαχείρισης Δεδομένων για Βιώσιμη Φόρτιση Ηλεκτρικών Οχημάτων
+ Soteris Constantinou (University of Cyprus)*; Constantinos Costa (Rinnoco Ltd); Andreas Konstantinidis (Frederick University); + Mohamed Mokbel (University of Minnesota - Twin Cities); Demetrios Zeinalipour-Yazti (University of Cyprus) + + Click to display the abstract + + Σε αυτό το άρθρο, παρουσιάζουμε ένα καινοτόμο πλαίσιο διαχείρισης δεδομένων για βιώσιμη φόρτιση Ηλεκτρικών Οχημάτων (ΗΟ) με την ονομασία EcoCharge. + Συγκεκριμένα, το EcoCharge εφαρμόζει ένα επερώτημα Continuous k-Nearest Neighbor, όπου η συνάρτηση απόστασης υπολογίζεται χρησιμοποιώντας Εκτιμώμενα Στοιχεία (Estimated Components - EC), + και το ονομάζουμε CkNN-EC. Ένα ΕC ορίζει μια συνάρτηση που μπορεί να έχει ασαφή τιμή βάσει ορισμένων εκτιμήσεων. Συγκεκριμένα ΕC που χρησιμοποιούνται σε αυτή τη δουλειά είναι: + (i) η (διαθέσιμη καθαρή) ενέργεια στον φορτιστή, που εξαρτάται από τις εκτιμώμενες καιρικές συνθήκες, (ii) η διαθεσιμότητα του φορτιστή, που εξαρτάται + από τα εκτιμώμενα χρονοδιαγράμματα που δείχνουν πότε ο φορτιστής είναι κατειλημμένος, και (iii) το κόστος παράκαμψης, που είναι ο χρόνος για να φτάσει + κανείς στον φορτιστή ανάλογα με την εκτιμώμενη κίνηση. Το πλαίσιο μας συνδυάζει τους πολλαπλούς μη-αντικρουόμενους στόχους σε μια συνάρτηση + βελτιστοποίησης παράγοντας μια κατάταξη φορτιστών. Ο αλγόριθμος χρησιμοποιεί κατώτερες και ανώτερες τιμές διαστημάτων που προέρχονται από τα + ΕC για να προτείνει τους φορτιστές ΗΟ με την υψηλότερη κατάταξη και να τους παρουσιάσει στους χρήστες μέσω μιας γεωγραφικής πληροφοριακής εφαρμογής. + Παρουσιάζουμε το EcoCharge χρησιμοποιώντας ένα πλήρες σύστημα που αναπτύχθηκε με τη βιβλιοθήκη Leaflet - OpenStreetMap. Στο σενάριο της επίδειξής μας, οι + συμμετέχοντες έχουν την ευκαιρία να παρατηρήσουν μέσω κινητών συσκευών τα οφέλη του EcoCharge, προσομοιώνοντας την εκτέλεσή του σε διάφορα προγραμματισμένα + ταξίδια με πραγματικά δεδομένα. + + |
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+ Apache Wayang: A Unified Data Analytics Framework
+ Kaustubh Beedkar (IIT Delhi); Bertty Contreras-Rojas (QCRI); Haralampos Gavriilidis (Technische Universität Berlin); + Zoi Kaoudi (IT University of Copenhagen)*; Volker Markl (Technische Universität Berlin); Jorge Arnulfo Quiané Ruiz (IT University of Copenhagen) + + Click to display the abstract + + The large variety of specialized data processing platforms and the increased complexity of data analytics has led to the need for + unifying data analytics within a single framework. Such a framework should free users from the burden of (i) choosing the right + platform(s) and (ii) gluing code between the different parts of their pipelines. Apache Wayang (Incubating) is the only open-source + framework that provides a systematic solution to unified data analytics by integrating multiple heterogeneous data processing platforms. + It achieves that by decoupling applications from the underlying platforms and providing an optimizer so that users do not have to specify + the platforms on which their pipeline should run. Wayang provides a unified view and processing model, effectively integrating the hodgepodge + of heterogeneous platforms into a single framework with increased usability without sacrificing performance and total cost of ownership. + In this paper, we present the architecture of Wayang, describe its main components, and give an outlook on future directions. + + |
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+ Demonstrating 𝜆-Tune: Exploiting Large Language Models for Workload-Adaptive Database System Tuning
+ Victor Giannakouris (Cornell University)* + + Click to display the abstract + + We demonstrate 𝜆-Tune, a tool that leverages Large Language Models (LLMs) for automated, workload-adaptive database system tuning. + 𝜆-Tune harnesses the ability of LLMs to process and comprehend arbitrary textual data in a zero-shot manner, employing a workload-adaptive + optimization approach. Given a database system, the hardware specifications, and a set of queries, 𝜆-Tune generates prompts to retrieve + configuration recommendations for the tuning knobs and the physical design, tailored to the specific system and workload. + Our framework utilizes a workload compression approach that extracts and includes in the prompt only the most insightful workload characteristics, + while the prompt size can be adjusted by a user defined token budget. Utilizing the zero-shot capabilities of LLMs, 𝜆-Tune outperforms other + LLM and machine learning-enhanced database tuning baselines, which rely on time-consuming tuning and training phases, as well as expensive + hardware, such as GPUs. During demonstration, users will be able to experiment with 𝜆-Tune to tune Postgres and MySQL, as well as explore + and modify the configurations retrieved by the LLM in an interactive way through 𝜆-Tune’s user interface. + + |
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+ F2: Designing a Key-Value Store for Large Skewed Workloads
+ Konstantinos Kanellis (University of Wisconsin-Madison)* + + Click to display the abstract + + Today's key-value stores are either disk-optimized, focusing on large data and saturating device IOPS, or memory-optimized, + focusing on high throughput with linear thread scaling assuming plenty of main memory. However, many practical workloads demand high + performance for read and write working sets that are much larger than main memory, over a total data size that is even larger. + They require judicious use of memory and disk, and today's systems do not handle such workloads well. We propose F2, + a new key-value store design based on compartmentalization. It consists of five key components that work together in + well-defined ways to achieve high throughput, saturating disk and memory bandwidths, while incurring low disk read + and write amplification. A key design characteristic of F2 is that it separates the management of hot and cold data, + across the read and write domains, and adapts the use of memory to optimize each case. Through a sequence of new + latch-free system constructs, F2 solves the key challenge of maintaining high throughput with linear thread + scalability in such a compartmentalized design. Detailed experiments on benchmark data validate our design's + superiority, in terms of throughput, over state-of-the-art key-value stores, when the available memory resources are scarce. + + |
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+ Achieving real-time relational OLTP and graph OLAP with OOPS
+ Antonio Maccioni (Huawei Research); Nikos Ntarmos (Edinburgh Research Center, Central Software Institute, Huawei)* + + Click to display the abstract + + It is common to encounter a layer of, possibly legacy, applications + sharing a relational database, with other applications needing to + continuously execute complex business logic on top. Such logic + enjoys a more natural expression through graph-based modeling, + as seen in fraud detection, data quality verification, marketing + prospecting, recommendation systems, and intrusion detection. At + Huawei, we frequently encounter these scenarios within embedded + and real-time systems such as smartphones, automotive, network + devices, domotics, and operative systems. Unfortunately, existing + solutions often fail to meet requirements for space efficiency, high + throughput, low latency, low power consumption, and handling + weak hardware. They typically resort to techniques such as query + rewriting or, worse, impractical on-the-fly data transformations + through expensive joins. To address these challenges, we have + developed a system called OOPS. This system allows applications to: + i) retain their legacy CRUD relational operations, ii) define a graph + view by specifying vertex and edge definitions based on relational + algebra statements independently of the source schema, iii) ingest + table records at high speed while other applications traverse the + graph view with consistency guarantees, and iv) allow continuous + querying of the previously mentioned traversals. + + |
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+ OBLIVIATOR: Oblivious Parallel Joins and other Operators in Shared Memory Environment
+ Apostolos Mavrogiannakis (UCSC)* + + Click to display the abstract + + In this paper, we introduce oblivious parallel operators designed for both non-foreign key and foreign key equi-joins. + Our approach is based on efficient oblivious primitives for compaction and sorting, combined with two novel oblivious algorithms + that we propose: (i) a new oblivious aggregation tree, which can be described as a variation of the parallel prefix sum, and (ii) + a new algorithm designed to obliviously expand the elements of a relation. + In the sequential setting, our oblivious join operator performs 4.6x- 5.14x faster than the prior state-the-art solution, introduced + in Krastnikov et al. (VLDB 2020) on datasets of size n=2^{24}. In the parallel setting, our algorithm achieves a speedup of up to roughly + 16x over the sequential version, when running with 32 threads (becoming up to 78x faster compared to Krastnikov's sequential algorithm). + Last but not least, our oblivious operators can be used independently to support other oblivious operators for relational database queries, + such as oblivious selection and oblivious group-by queries. " + + |
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+ How to Make your Duck Fly: Advanced Floating Point Compression to the Rescue
+ Panagiotis Liakos (Athens University of Economics and Business)*; Katia Papakonstantinopoulou (Athens University of Economics and Business); + Yannis Kotidis (Athens University of Economics and Business) + + Click to display the abstract + + The massive volumes of data generated in diverse domains, such as scientific computing, finance and environmental monitoring, hinder our ability + to perform multidimensional analysis at high speeds and also yield significant storage and egress costs. Applying compression algorithms + to reduce these costs is particularly suitable for column-oriented DBMSs, as the values of individual columns are usually similar and thus, + allow for effective compression. However, this has not been the case for binary floating point numbers, as the space savings achieved + by respective compression algorithms are usually very modest. We present here two lossless compression algorithms for floating point data, + termed Chimp and Patas, that attain impressive compression ratios and greatly outperform state-of-the-art approaches. + We focus on how these two algorithms impact the performance of DuckDB, a purpose-built embeddable database for interactive analytics. + Our demonstration will showcase how our novel compression approaches a) reduce storage requirements, + and b) improve the time needed to load and query data using DuckDB. + + |
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+ DataMingler: A Novel Approach to Data Virtualization
+ Damianos Chatziantoniou (Athens University of Economics and Business)*; Verena Kantere (University of Ottawa) + + Click to display the abstract + + A Data Virtual Machine (DVM) is a novel graph-based conceptual model, similar to the entity-relationship model, + representing existing data (persistent, transient, derived) of an organization. A DVM can be built quickly, agilely, + offering schematic flexibility to data engineers. Data scientists can visually define complex dataframe queries in + an intuitive and simple manner, which are evaluated within an algebraic framework. A DVM can be easily materialized + in any logical data model and can be ""reoriented'' around any node, offering a ""single view of any entity''. + In this paper we demonstrate DataMingler, a tool implementing DVMs. We argue that DVMs can have a significant + practical impact in analytics environments." + + |
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