From 1d4d8ae95e3c45efd44a4d05d3f4cd223ca502db Mon Sep 17 00:00:00 2001 From: tzerefos Date: Sun, 9 Jun 2024 11:36:49 +0300 Subject: [PATCH] Program draft v2 --- hdms/2024/assets/css/main_newest.css | 17 +- hdms/2024/assets/css/program.css | 7 + hdms/2024/assets/js/program.js | 11 + hdms/2024/index.html | 4 +- hdms/2024/program.html | 746 +++++++++++++++++++++------ 5 files changed, 623 insertions(+), 162 deletions(-) create mode 100644 hdms/2024/assets/css/program.css create mode 100644 hdms/2024/assets/js/program.js diff --git a/hdms/2024/assets/css/main_newest.css b/hdms/2024/assets/css/main_newest.css index ae239f8..593d946 100644 --- a/hdms/2024/assets/css/main_newest.css +++ b/hdms/2024/assets/css/main_newest.css @@ -1766,7 +1766,7 @@ } .inner { - max-width: 75em; + max-width: 80em; margin: 0 auto; } @@ -2506,6 +2506,10 @@ padding: 0.75em 0.75em; } + table td:first-child { + width: 10% ; + } + table th { color: #555; font-size: 0.9em; @@ -3792,14 +3796,17 @@ overflow-x:auto; } .program-session-titles{ - font-size: 1.2em; + font-size: 0.9em; + /* text-align: left; */ } .program-sessions{ - font-size: 1em; + font-size: 0.9em; + text-align: left; } .program-session-breaks{ - font-size: 1.2em; - color: red; + font-size: 0.9em; + /* text-align: left; */ + /* color: red; */ font-weight: 700; } diff --git a/hdms/2024/assets/css/program.css b/hdms/2024/assets/css/program.css new file mode 100644 index 0000000..6fae270 --- /dev/null +++ b/hdms/2024/assets/css/program.css @@ -0,0 +1,7 @@ +.paper-abstract{ + display: none; +} + +.toggle-abstract{ + cursor: pointer; +} \ No newline at end of file diff --git a/hdms/2024/assets/js/program.js b/hdms/2024/assets/js/program.js new file mode 100644 index 0000000..1953f29 --- /dev/null +++ b/hdms/2024/assets/js/program.js @@ -0,0 +1,11 @@ +// Javascript file that handles program.html functions + +function toggleAbstract(abstract) { +// Function that toggles upon click the abstract on the program section + var element = document.getElementById(abstract); + if (element.style.display !== "block" ) { + element.style.display = "block"; + } else { + element.style.display = "none"; + } +} \ No newline at end of file diff --git a/hdms/2024/index.html b/hdms/2024/index.html index d23ed79..d4e119e 100644 --- a/hdms/2024/index.html +++ b/hdms/2024/index.html @@ -1122,8 +1122,8 @@

Fusce pellentesque tempus

Sponsors
- TBD - + + Inos
diff --git a/hdms/2024/program.html b/hdms/2024/program.html index aef8352..e2f0b94 100644 --- a/hdms/2024/program.html +++ b/hdms/2024/program.html @@ -10,6 +10,7 @@ + @@ -62,89 +63,320 @@

Event - 8:00 - 8:45am + 8:00 - 9:15am Registration - 8:45 - 9:00am + 9:15 - 9:30am Opening Remarks 9:30 - 10:00am - keynote 1 - - - Session 1: "Title" Chair: Chair - - - 10:00 - 10:15am - Paper 1 - - - 10:15 - 10:30am - Paper 2 - - - 10:30 - 10:45am - Paper 3 - - - 10:45 - 11:00am - Paper 4 - - - 11:00 - 11:15am - Paper 5 - - - 11:15 - 11:30am - Paper 6 - - - 30 min break - - - Session 2: "Title" Chair: Chair - - - 12:00 - 12:15pm - Paper 1 - - - 12:15 - 12:30pm - Paper 2 - - - 12:30 - 12:45pm - Paper 3 - - - 12:45 - 1:00pm - Paper 4 - - - 1:00 - 1:15pm - Paper 5 - - - 1:15 - 1:30pm - Paper 6 + Keynote 1: Stavros Papadopoulos (TileDB) + + + Session 1: "Modern Data Processing" + + + + Amazon Redshift Re-invented +
Armenatzoglou, Nikos*; Pandis, Ippokratis; Polychroniou, Orestis; Parchas, Panos +
Click to display the abstract + + In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift, the first fully-managed, + petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze + large volumes of data using existing business intelligence tools. This cloud service was a significant leap from the traditional + on-premise data warehousing solutions, which were expensive, not elastic, and required significant expertise to tune and operate. + Customers embraced Amazon Redshift and it became the fastest growing service in AWS. Today, tens of thousands of customers use Redshift + in AWS’s global infrastructure to process exabytes of data daily. +

+ In the last few years, the use cases for Amazon Redshift have evolved and in response, the service has delivered and continues to + deliver a series of innovations that delight customers. Through architectural enhancements, Amazon Redshift has maintained its industry-leading + performance. Redshift improved storage and compute scalability with innovations such as tiered storage, multi-cluster auto-scaling, cross-cluster + data sharing and the AQUA query acceleration layer. Autonomics have made Amazon Redshift easier to use. Amazon Redshift Serverless is the + culmination of autonomics effort, which allows customers to run and scale analytics without the need to set up and manage data warehouse infrastructure. + Finally, Amazon Redshift extends beyond traditional datawarehousing workloads, by integrating with the broad AWS ecosystem with features such + as querying the data lake with Spectrum, semistructured data ingestion and querying with PartiQL, streaming ingestion from Kinesis and MSK, Redshift ML, + federated queries to Aurora and RDS operational databases, and federated materialized views." + + + + + + QPSeeker: An Efficient Neural Planner combining both data and queries through Variational Inference +
Tsapelas, Christos*; Koutrika, Georgia +
Click to display the abstract + + Recently, deep learning methods have been applied on many aspects of the query optimization process, such as cardinality estimation and query + execution time prediction, but very few tackle multiple aspects of the optimizer at the same time or com- bine both the underlying data and a + query workload. QPSeeker takes a step towards a neural database planner, encapsulating the information of the data and the given workload to + learn the distributions of cardinalities, costs and execution times of the query plan space. At inference, when a query is submitted to the + database, QPSeeker uses its learned cost model and traverses the query plan space using Monte Carlo Tree Search to provide an execution plan + for the query. + + + + + + Dalton: Learned Partitioning for Distributed Data Streams +
Zapridou, Eleni*; Mytilinis, Ioannis; Ailamaki, Anastasia +
Click to display the abstract + + "To sustain the input rate of high-throughput streams, modern stream processing systems rely on parallel execution. However, skewed data yield + imbalanced load assignments and create stragglers that hinder scalability. Deciding on a static partitioning for a given set of ""hot"" keys is + not sufficient as these keys are not known in advance, and even worse, the data distribution can change unpredictably. Existing algorithms + either optimize for a specific distribution or, in order to adapt, assume a centralized partitioner that processes every incoming tuple and + observes the whole workload. However, this is not realistic in a distributed environment, where multiple parallel upstream operators exist, as + the centralized partitioner itself becomes the bottleneck and limits scalability. +

+ In this work, we propose Dalton: a lightweight, adaptive, yet scalable partitioning operator that relies on reinforcement learning. + By memoizing state and dynamically keeping track of recent experience, Dalton: i) adjusts its policy at runtime and quickly adapts to the workload, ii) + avoids redundant computations and minimizes the per-tuple partitioning overhead, and iii) efficiently scales out to multiple instances that learn + cooperatively and converge to a joint policy. Our experiments indicate that Dalton scales regardless of the input data distribution and sustains + 1.3x - 6.7x higher throughput than existing approaches." + + + + + + LlamaTune: Sample-Efficient DBMS Configuration Tuning +
Kanellis, Konstantinos* +
Click to display the abstract + + Tuning a database system to achieve optimal performance on a given workload is a long-standing problem in the database community. + A number of recent works have leveraged ML-based approaches to guide the sampling of large parameter spaces (hundreds of tuning knobs) + in search for high performance configurations. Looking at Microsoft production services operating millions of databases, sample efficiency + emerged as a crucial requirement to use tuners on diverse workloads. This motivates our investigation in LlamaTune, a tuner design that + leverages domain knowledge to improve the sample efficiency of existing optimizers. LlamaTune employs an automated dimensionality reduction + technique based on randomized projections, a biased-sampling approach to handle special values for certain knobs, and knob values bucketization, + to reduce the size of the search space. LlamaTune compares favorably with the state-of-theart optimizers across a diverse set of workloads. + It identifies the best performing configurations with up to 11× fewer workload runs, and reaching up to 21% higher throughput. We also show + that benefits from LlamaTune generalize across both BO-based and RL-based optimizers, as well as different DBMS versions. While the journey + to perform database tuning at cloud-scale remains long, LlamaTune goes a long way in making automatic DBMS tuning practical at scale." + + + + + + Pre-trained Embeddings for Entity Resolution: An Experimental Analysis +
Zeakis, Alexandros*; Papadakis, George; Skoutas, Dimitrios; Koubarakis, Manolis +
Click to display the abstract + + Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. + This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained embeddings have been tested, with the + most popular ones being fastText and variants of the BERT model. However, there is no detailed analysis of their pros and cons. + To cover this gap, we perform a thorough experimental analysis of 12 popular language models over 17 established benchmark datasets. + First, we assess their vectorization overhead for converting all input entities into dense embeddings vectors. Second, we investigate + their blocking performance, performing a detailed scalability analysis, and comparing them with the state-of-the-art deep learning-based + blocking method. Third, we conclude with their relative performance for both supervised and unsupervised matching. Our experimental + results provide novel insights into the strengths and weaknesses of the main language models, facilitating researchers and practitioners + to select the most suitable ones in practice. + + + + + + YeSQL: "you extend SQL" with rich and highly performant user-defined functions in relational databases +
Foufoulas, Yannis*; Simitsis, Alkis; Stamatogiannakis, Lefteris; Ioannidis, Yannis +
Click to display the abstract + + The diversity and complexity of modern data management applications have led to the extension of the relational paradigm with syntactic + and semantic support for User-Defined Functions (UDFs). Although well-established in traditional DBMS settings, UDFs have become central + in many application contexts as well, such as data science, data analytics, and edge computing. Still, a critical limitation of UDFs is + the impedance mismatch between their evaluation and relational processing. In this paper, we present YeSQL, an SQL extension with rich + UDF support along with a pluggable architecture to easily integrate it with either server-based or embedded database engines. YeSQL currently + supports Python UDFs fully integrated with relational queries as scalar, aggregator, or table functions. Key novel characteristics of YeSQL + include easy implementation of complex algorithms and several performance enhancements, including tracing JIT compilation of Python UDFs, + parallelism and fusion of UDFs, stateful UDFs, and seamless integration with a database engine. Our experimental analysis showcases the + usability and expressiveness of YeSQL and demonstrates that our techniques of minimizing context switching between the relational engine + and the Python VM are very effective and achieve significant speedups up to 68x in common, practical use cases compared to earlier + approaches and alternative implementation choices. + + + + + + Joint Source and Schema Evolution: Insights from a Study of 195 FOSS Projects +
Vassiliadis, Panos* +
Click to display the abstract + + In this paper, we address the problem of the co-evolution of Free Open Source Software projects with the relational schemata + that they encompass. We exploit a data set of 195 publicly available schema histories of FOSS projects hosted in Github, + for which we locally cloned their respective project and measured their evolution progress. Our first research question asks + which percentage of the projects demonstrates a “hand-in-hand” schema and source code co-evolution? To address this question, + we defined synchronicity by allowing a bounded amount of lag between the cumulative evolution of the schema and the entire project. + A core finding is that there are all kinds of behaviors with respect to project and schema co-evolution, resulting in only a small + number of projects where the evolution of schema and project progress in sync. Moreover, we discovered that after exceeding a 5-year + threshold of project life, schemata gravitate to lower rates of evolution, which practically means that, with time, the schemata stop + evolving as actively as they originally did. To answer a second question, on whether evolution comes early in the life of a schema, + we measured how often does the cumulative progress of schema evolution exceed the respective progress of source change, as well as + the respective progress of time. The results indicate that a large majority of schemata demonstrates early advance of schema change + with respect to code evolution, and, an even larger majority is also demonstrating an advance of schema evolution with respect to time, too. + Third, we asked at which time point in their lives do schemata attain a substantial percentage of their evolution. A large number of + projects attracts a large percentage of their schema evolution disproportionately early with respect to their project life span. + Indicatively, 98 of the 195 projects attained 75% of the evolution in just the first 20% of their project’s lifetime." + + + + + + Adaptive Real-time Virtualization of Legacy ETL Pipelines in Cloud Data Warehouses +
Tsikoudis, Nikos* +
Click to display the abstract + + Extract, Transform, and Load (ETL) pipelines are widely used to ingest data into Enterprise Data Warehouse (EDW) systems. These pipelines + can be very complex and often tightly coupled to a given EDW, making it challenging to upgrade from a legacy EDW to a Cloud Data Warehouse (CDW). + This paper presents a novel solution for a transparent and fully-automated porting of legacy ETL pipelines to CDW environments." + + + + + + 11:30 - 12:00pm + Break + + + Session 2: "Time-Series, Mobile, Scientific Databases" Chair: Chair + + + + TIMBER: On supporting data pipelines in Mobile Cloud Environments +
Tomaras, Dimitris; Tsenos, Michalis; Kalogeraki, Vana; Gunopulos, Dimitrios + + + + + + Mobility Data Science: Perspectives and Challenges +
Mokbel, Mohamed; Sakr, Mahmoud A; Xiong, Li; Züfle, Andreas; Theodoridis, Yannis* +
Click to display the abstract + + Mobility data captures the locations of moving objects such as humans, animals, and cars. With the availability of GPS equipped + mobile devices and other inexpensive location-tracking technologies, mobility data is collected ubiquitously. In recent years, + the use of mobility data has demonstrated significant impact in various domains including traffic management, urban planning, and + health sciences. In this paper, we present the domain of mobility data science. Towards a unified approach to mobility data science, + we present a pipeline having the following components: mobility data collection, cleaning, analysis, management, and privacy. + For each of these components, we explain how mobility data science differs from general data science, we survey the current state of + the art, and describe open challenges for the research community in the coming years. + + + + + + SIESTA: A Scalable Infrastructure of Sequential Pattern Analysis +
Mavroudopoulos, Ioannis*; Gounaris, Anastasios +
Click to display the abstract + + Sequential pattern analysis has become a mature topic with a lot of techniques for a variety of sequential pattern mining-related problems. + Moreover, tailored solutions for specific domains, such as business process mining, have been developed. However, there is a gap in the + literature for advanced techniques for efficient detection of arbitrary sequences in large collections of activity logs. In this work, + we introduce the SIESTA ( S calable i nfrastructur e of s equential pa t tern a nalysis) solution making a threefold contribution: (i) + we employ a novel architecture that relies on inverted indices during preprocessing and we introduce an advanced query processor that + can detect and explore arbitrary patterns efficiently; (ii) we discuss and evaluate different configurations to optimize both the + preprocessing and the querying phase; and (iii) we present evaluation results competing against representatives of the state-of-the-art + with a focus on Big Data. The experimental results are particularly encouraging, e.g., when all methods are deployed in a cluster and + the volume of the data is increased, SIESTA creates the indices in almost half the time compared to the state-of-the-art Elasticsearch-based + solution, while also yielding faster query responses than all its competitors by up to 1 order of magnitude. + + + + + + Exploring unsupervised anomaly detection for vehicle predictive maintenance with partial information +
Giannoulidis, Apostolos*; Gounaris, Anastasios; Constantinou, Ioannis +
Click to display the abstract + + Predicting the need for maintenance in vehicle fleets enhances safety and lessens the downtime. While vehicle manufacturers provide + built-in alert systems, these often fail to alert the driver when something goes wrong. However, harnessing the power of data analytics + and real-time signals can solve this problem. In this work, we describe a challenging real-world setting with scarce and partial data + of failures. We propose a non-supervised approach that detects behavioral changes related to failures avoiding using the raw signals + directly to cope with driving behavior and weather volatility. Our solution calculates the differences in the correlations of collected + signals between two periods and dynamically creates reference profiles of normal operational conditions tolerating noise. The initial + experiments are particularly promising, e.g., we achieve 78\% precision detecting nearly half of the failures outperforming the behavior + of a state-of-the-art deep learning technique. More importantly, we consider our solution as a specific instantiation of a broader + framework, for which we thoroughly evaluate a broad range of alternatives. + + + + + + On Vessel Location Forecasting and the Effect of Federated Learning +
Tritsarolis, Andreas*; Pelekis, Nikos; Bereta, Konstantina; Zissis, Dimitrios; Theodoridis, Yannis +
Click to display the abstract + + The wide spread of Automatic Identification System (AIS) has motivated several maritime analytics operations. Vessel Location Forecasting (VLF) + is one of the most critical operations for maritime awareness. However, accurate VLF is a challenging problem due to the complexity and + dynamic nature of maritime traffic conditions. Furthermore, as privacy concerns and restrictions have grown, training data has become + increasingly fragmented, resulting in dispersed databases of several isolated data silos among different organizations, which in turn + decreases the quality of learning models. In this paper, we propose an efficient VLF solution based on LSTM neural networks, in two + variants, namely Nautilus and FedNautilus for the centralized and the federated learning approach, respectively. We also demonstrate + the superiority of the centralized approach with respect to current state of the art and discuss the advantages and disadvantages + of the federated against the centralized approach. + + + + + + Collision Risk Assessment and Forecasting on Maritime Data (Industrial Paper) +
Tritsarolis, Andreas*; Murray, Brian; Pelekis, Nikos; Theodoridis, Yannis +
Click to display the abstract + + The wide spread of the Automatic Identification System (AIS) and related tools has motivated several maritime analytics operations. + One of the most critical operations for the purpose of maritime safety is the so-called Vessel Collision Risk Assessment and + Forecasting (VCRA/F), with the difference between the two lying in the time horizon when the collision risk is calculated: either + at current time by assessing the current collision risk (i.e., VCRA) or in the (near) future by forecasting the anticipated locations + and corresponding collision risk (i.e., VCRF). Accurate VCRA/F is a difficult task, since maritime traffic can become quite volatile + due to various factors, including weather conditions, vessel manoeuvres, etc. Addressing this problem by using complex models + introduces a trade-off between accuracy (in terms of quality of assessment / forecasting) and responsiveness. In this paper, we + propose a deep learning-based framework that discovers encountering vessels and assesses/predicts their corresponding collision + risk probability, in the latter case via state-of-the-art vessel route forecasting methods. Our experimental study on a real-world + AIS dataset demonstrates that the proposed framework balances the aforementioned trade-off while presenting up to 70% improvement + in R2 score, with an overall accuracy of around 96% for VCRA and 77% for VCRF. + + + + + + Visualization-aware Time Series Min-Max Caching with Error Bound Guarantees +
Maroulis , Stavros *; Stamatopoulos, Vassilis; Papastefanatos, George; Terrovitis, Manolis +
Click to display the abstract + + This paper addresses the challenges in interactive visual exploration of large multi-variate time series data. Traditional data + reduction techniques may improve latency but can distort visualizations. State-of-the-art methods aimed at 100% accurate + visualization often fail to maintain interactive response times or require excessive pre-processing and additional storage. + We propose an in-memory adaptive caching approach, MinMaxCache, that efficiently reuses previous query results to accelerate + visualization performance within accuracy constraints. MinMaxCache fetches data at adaptively determined aggregation granularities + to maintain interactive response times and generate approximate visualizations with accuracy guarantees. Our results show that it + is up to 10 times faster than current solutions without significant accuracy compromise. + + + + + + Chimp: Efficient Lossless Floating Point Compression for Time Series Databases +
Liakos, Panagiotis; Papakonstantinopoulou, Katia; Kotidis, Yannis +
Click to display the abstract + + + + - 90 min lunch break + 1:30 - 3:00pm + Lunch break 3:00 - 3:30pm - keynote 2 - - - Session 3: "Title" Chair: Chair + Industry Session - 3:30 - 3:45pm - Paper 1 + 3:30 - 4:00pm + Keynote 2: Verena Kantere - + - 30 min break + 4:00-4:30pm + Break - Session 4: "Panel/Poster session" Chair: Chair + 4:30-5:30pm + Panel AI & DB in Industry - 5:30-7:00pm - Panel / Posters + 5:45-7:00pm + Posters Move to dinner -

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keynote 1
Session 1: "Title" Chair: Chair
10:00 - 10:15amPaper 1
10:15 - 10:30amPaper 2
10:30 - 10:45amPaper 3
10:45 - 11:00amPaper 4
11:00 - 11:15amPaper 5
11:15 - 11:30amPaper 6
30 min break
Session 2: "Title" Chair: Chair
12:00 - 12:15pmPaper 1
12:15 - 12:30pmPaper 2
12:30 - 12:45pmPaper 3
12:45 - 1:00pmPaper 4
1:00 - 1:15pmPaper 5
1:15 - 1:30pmPaper 6
90 min lunch break
3:00 - 4:00pm Mentoring
30 min break keynote 3: Ippokratis Pandis
Session 1: "Query Processing & Execution" Chair: Chair
Predicate Transfer: Efficient Pre-Filtering on Multi-Join Queries +
Koutris, Paraschos*; Yu, Xiangyao; Zhao, Hangdong; Yang, Yifei +
Click to display the abstract + + This paper presents predicate transfer, a novel method that optimizes join performance by pre-filtering tables to reduce the join input sizes. + Predicate transfer generalizes Bloom join, which conducts pre-filtering within a single join operation, to multi-table joins such that the + filtering benefits can be significantly increased. Predicate transfer is inspired by the seminal theoretical results by Yannakakis, which uses + semi-joins to pre-filter acyclic queries. Predicate transfer generalizes the theoretical results to any join graphs and use Bloom filters to replace + semi-joins leading to significant speedup. Evaluation shows predicate transfer can outperform Bloom join by 3.3× on average on the TPC-H benchmark. + +
Foreign Keys Open the Door for Faster Incremental View Maintenance +
Svingos, Christoforos* +
Click to display the abstract + + Serverless cloud-based warehousing systems enable users to create materialized views in order to speed up predictable and repeated query workloads. + Incremental view maintenance (IVM) minimizes the time needed to bring a materialized view up-to-date. It allows the refresh of a materialized view + solely based on the base table changes since the last refresh. In serverless cloud-based warehouses, IVM uses computations defined as SQL scripts + that update the materialized view based on updates to its base tables. However, the scripts set up for materialized views with inner joins are not + optimal in the presence of foreign key constraints. For instance, for a join of two tables, the state of the art IVM computations use a UNION ALL + operator of two joins - one computing the contributions to the join from updates to the first table and the other one computing the remaining + contributions from the second table. Knowing that one of the join keys is a foreign-key would allow us to prune all but one of the UNION ALL branches + and obtain a more efficient IVM script. In this work, we explore ways of incorporating knowledge about foreign key into IVM in order to speed up + its performance. Experiments in Redshift showed that the proposed technique improved the execution times of the whole refresh process up to 2 times, + and up to 2.7 times the process of calculating the necessary changes that will be applied into the materialized view. + +
SH2O: Efficient Data Access for Work-Sharing Databases +
Mytilinis, Ioannis*; Sioulas, Panagiotis; Ailamaki, Anastasia +
Click to display the abstract + + Interactive applications require processing tens to hundreds of concurrent analytical queries within tight time constraints. In such setups, where + high concurrency causes contention, work-sharing databases are critical for improving scalability and for bounding the increase in response time. + However, as such databases share data access using full scans and expensive shared filters, they suffer from a data-access bottleneck that jeopardizes + interactivity. We present SH2O: a novel data-access operator that addresses the data-access bottleneck of work-sharing + databases. SH2O is based on the idea that an access pattern based on judiciously selected multidimensional + ranges can replace a set of shared filters. To exploit the idea in an efficient and scalable manner, SH2O uses a + three-tier approach: i) it uses spatial indices to efficiently access the ranges without overfetching, ii) it uses an + optimizer to choose which filters to replace such that it maximizes cost-benefit for index accesses, and iii) it + exploits partitioning schemes and independently accesses each data partition to reduce the number of filters + in the access pattern. Furthermore, we propose a tuning strategy that chooses a partitioning and indexing + scheme that minimizes SH2O’s cost for a target workload. Our evaluation shows a speedup of 1.8 − 22.2 for + batches of hundreds of data-access-bound queries. + +
Efficient Computation of Quantiles over Joins +
Tziavelis, Nikolaos* +
Click to display the abstract + + We consider the complexity of answering Quantile Join Queries, which ask for the answer at a specified relative position (e.g., 50% for the median) + under some ordering over the answers to an ordinary Join Query. The goal is to avoid materializing the set of all join answers, and to achieve close + to linear time in the size of the database, regardless of the total number of answers. As we show, this is not possible for all queries + (under certain assumptions in fine-grained complexity) and it crucially depends on both the join structure and the desired order. We establish a + dichotomy that precisely characterizes the (self-join-free) queries that can be handled efficiently for common ranking functions, such as a sum + of attribute weights. We also provide an algorithm that can handle all known tractable cases by iteratively using a "trimming'' subroutine which + removes query answers that are higher or lower (according to the ranking function) than a certain answer determined as the "pivot". + + +
Raster Intervals: An Approximation Technique for Polygon Intersection Joins +
Georgiadis, Thanasis*; Mamoulis, Nikos +
Click to display the abstract + + Many data science applications, most notably Geographic Information Systems, require the computation of spatial joins between large object collections. + The objective is to find pairs of objects that intersect, i.e., share at least one common point. The intersection test is very expensive especially + for polygonal objects. Therefore, the objects are typically approximated by their minimum bounding rectangles (MBRs) and the join is performed in two + steps. In the filter step, all pairs of objects whose MBRs intersect are identified as candidates; in the refinement step, each of the candidate pairs + is verified for intersection. The refinement step has been shown notoriously expensive, especially for polygon-polygon joins, constituting the bottleneck + of the entire process. We propose a novel approximation technique for polygons, which (i) rasterizes them using a fine grid, (ii) models groups of + nearby cells that intersect a polygon as an interval, and (iii) encodes each interval by a bitstring that captures the overlap of each cell in it with + the polygon. We also propose an efficient intermediate filter, which is applied on the object approximations before the refinement step, to avoid it + for numerous object pairs. Via experimentation with real data, we show that the end-to-end spatial join cost can be reduced by up to one order of + magnitude with the help of our filter and by at least three times compared to using alternative intermediate filters. + +
In-Situ Cross-Database Query Processing +
Gavriilidis, Haralampos*; Beedkar, Kaustubh; Quiané Ruiz, Jorge Arnulfo; Markl, Volker +
Click to display the abstract + + Today’s organizations utilize a plethora of heterogeneous and autonomous DBMSes, many of those being spread across different geo-locations. It is + therefore crucial to have effective and efficient cross-database query processing capabilities. We present XDB, an efficient middleware system that + runs cross-database analytics over existing DBMSes. In contrast to traditional query processing systems, XDB does not rely on any mediating execution + engine to perform cross-database operations (e.g., joining data from two DBMSes). It delegates an entire query execution including cross-database + operations to underlying DBMSes. At its core, it comprises an optimizer and a delegation engine: the optimizer rewrites cross-database queries into + a delegation plan, which captures the semantics as well as the mechanics of a fully decentralized query execution; the delegation engine then deploys + the plan to the underlying DBMSes via their declarative interfaces. Our experimental study based on the TPC-H benchmark data shows that XDB outperforms + state-of-the-art systems (Garlic and Presto) by up to 6× in terms of runtime and up to 3 orders of magnitude in terms of data transfer. + +
P Cost-based Data Prefetching and Scheduling in Big Data Platforms over Tiered Storage Systems +
Herodotou, Herodotos *; Kakoulli, Elena +
Click to display the abstract + + The use of storage tiering is becoming popular in data-intensive compute clusters due to the recent advancements in storage technologies. + The Hadoop Distributed File System, for example, now supports storing data in memory, SSDs, and HDDs, while OctopusFS and hatS offer fine-grained + storage tiering solutions. However, current big data platforms (such as Hadoop and Spark) are not exploiting the presence of storage tiers and + the opportunities they present for performance optimizations. Specifically, schedulers and prefetchers will make decisions only based on data + locality information and completely ignore the fact that local data are now stored on a variety of storage media with different performance + characteristics. This article presents Trident, a scheduling and prefetching framework that is designed to make task assignment, resource scheduling, + and prefetching decisions based on both locality and storage tier information. Trident formulates task scheduling as a minimum cost maximum + matching problem in a bipartite graph and utilizes two novel pruning algorithms for bounding the size of the graph, while still guaranteeing + optimality. In addition, Trident extends YARN’s resource request model and proposes a new storage-tier-aware resource scheduling algorithm. + Finally, Trident includes a cost-based data prefetching approach that coordinates with the schedulers for optimizing prefetching operations. + Trident is implemented in both Spark and Hadoop and evaluated extensively using a realistic workload derived from Facebook traces as well as + an industry-validated benchmark, demonstrating significant benefits in terms of application performance and cluster efficiency. + + +
DIAERESIS: RDF Data Partitioning and Query Processing on SPARK +
Troullinou, Georgia; Agathangelos, Giannis; Kondylakis, Haridimos*; Stefanidis, Kostas; Plexousakis, Dimitris +
Click to display the abstract + + The explosion of the web and the abundance of linked data demand effective and efficient methods for storage, management, and querying. Apache Spark + is one of the most widely used engines for big data processing, with more and more systems adopting it for efficient query answering. Existing + approaches exploiting Spark for querying RDF data, adopt partitioning techniques for reducing the data that need to be accessed in order to improve + efficiency. However, simplistic data partitioning fails, on one hand, to minimize data access and on the other hand to group data usually queried + together. This is translated into limited improvement in terms of efficiency in query answering. In this paper, we present DIAERESIS, a novel + platform that accepts as input an RDF dataset and effectively partitions it, minimizing data access and improving query answering efficiency. + To achieve this, DIAERESIS first identifies the top-k most important schema nodes, i.e., the most important classes, as centroids and distributes + the other schema nodes to the centroid they mostly depend on. Then, it allocates the corresponding instance nodes to the schema nodes they are + instantiated under. Our algorithm enables fine-tuning of data distribution, significantly reducing data access for query answering. We experimentally + evaluate our approach using both synthetic and real workloads, strictly dominating existing state-of-the-art, showing that we improve query answering + in several cases by orders of magnitude. + +
11:30 - 12:00pm Break
Session 2: "Indexing & Similarity Search" Chair: Chair
LIT: Lightning-fast In-memory Temporal Indexing
+ George Christodoulou (TU Delft); Panagiotis Bouros (Johannes Gutenberg University Mainz); Nikos Mamoulis (University of Ioannina)* +
Click to display the abstract + + We study the problem of temporal database indexing, i.e., indexing versions of a database table in an evolving database. With the larger and + cheaper memory chips nowadays, we can afford to keep track of all versions of an evolving table in memory. This raises the question of how to + index such a table effectively. We depart from the classic indexing approach, where both current (i.e., live) and past (i.e., dead) data versions + are indexed in the same data structure, and propose LIT, a hybrid index, which decouples the management of the current and past states of the indexed + column. LIT includes optimized indexing modules for dead and live records, which support efficient queries and updates, and gracefully combines them. + We experimentally show that LIT is orders of magnitude faster than the state-of-the-art temporal indices. Furthermore, we demonstrate that LIT uses + linear space to the number of record indexed versions, making it suitable for main-memory temporal data management. + +
Efficient Semantic Similarity Search over Spatio-textual Data +
George S. Theodoropoulos (University of Piraeus); Kjetil Nørvåg (Norwegian University of Science and Technology); Christos Doulkeridis + (University of Pireaus)* +
Click to display the abstract + + In this paper, we address the problem of semantic similarity search over spatio-textual data. In contrast with most existing works on spatial-keyword + search that rely on exact matching of query keywords to textual descriptions, we focus on semantic textual similarity using word embeddings, which + have been shown to capture semantic similarity exceptionally well in practice. To support efficient k-nearest neighbor (k-NN) search over a weighted + combination of spatial and semantic dimensions, we propose a novel indexing approach (called CSSI) that ensures correctness of results, alongside its + approximate variant (called CSSIA) that introduces a small amount of error in exchange for improved performance. Both variants are based on a hybrid + clustering scheme that jointly indexes the spatial and textual/semantic information, achieving high pruning percentages and improved performance and + scalability. + +
Ένα Πλαίσιο Διαχείρισης Δεδομένων για Συνεχή kNN Κατάταξη Φορτιστών Ηλεκτρικών Οχημάτων με Εκτιμώμενα Στοιχεία +
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 πλαίσιο διαχείρισης δεδομένων του οποίου ο στόχος είναι να επιτρέψει στους οδηγούς να επαναφορτίζουν τα + Ηλεκτρικά Οχήματά τους (ΗΟ) από τους πιο φιλικούς προς το περιβάλλον φορτιστές. Ειδικότερα, στόχος ειναι να μεγιστοποιούν οι οι φορτιστές την + ιδιοκατανάλωση ανανεώσιμων πηγών (π.χ., ηλιακής ενέργειας), ελαχιστοποιώντας με αυτόν τον τρόπο την παραγωγή CO2 και επίσης την ανάγκη για ακριβές + στάσιμες μπαταρίες στο ηλεκτρικό δίκτυο για την αποθήκευση ανανεώσιμης ενέργειας. Μοντελοποιούμε το πρόβλημά μας ως ένα επερώτημα Continuous + k-Nearest Neighbor, όπου η συνάρτηση απόστασης υπολογίζεται χρησιμοποιώντας Εκτιμώμενα Στοιχεία (Estimated Components - EC), και το ονομάζουμε + CkNN-EC. Ένα EC ορίζει μια συνάρτηση που μπορεί να έχει ασαφή τιμή βάσει ορισμένων εκτιμήσεων. Συγκεκριμένα EC 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 κατειλημμένοι, και (iii) το κόστος παράκαμψης, + που είναι ο χρόνος για να φτάσει κανείς στον φορτιστή ανάλογα με την εκτιμώμενη κίνηση. Δημιουργήσαμε το σύστημα EcoCharge που συνδυάζει τους + πολλαπλούς μη-αντικρουόμενους στόχους σε μια συνάρτηση βελτιστοποίησης παράγοντας μια κατάταξη φορτιστών. Ο βασικός μας αλγόριθμος χρησιμοποιεί + κατώτερες και ανώτερες τιμές διαστημάτων που προέρχονται από τα EC για να προτείνει τους φορτιστές με την υψηλότερη κατάταξη και να τους + παρουσιάσει στους χρήστες μέσω μιας διεπαφής χαρτών. Η πειραματική μας αξιολόγηση με εκτενείς συνθετικά και πραγματικά δεδομένα μαζί με δεδομένα + φορτιστών από το Plugshare δείχνει ότι το EcoCharge πληροί τους στόχους της συνάρτησης με αποτελεσματικό τρόπο, επιτρέποντας συνεχή και ακριβές + επαναυπολογισμό στις διάφορες συσκευές. + +
OmniSketch: Efficient Multi-Dimensional High-Velocity Stream Analytics with Arbitrary Predicates +
Wieger R. Punter (TU Eindhoven); Odysseas Papapetrou (TU Eindhoven)*; Minos Garofalakis (ATHENA Research Center) +
Click to display the abstract + + A key need in different disciplines is to perform analytics over fast-paced data streams, similar in nature to the traditional OLAP analytics in relational + databases i.e., with filters and aggregates. Storing unbounded streams, however, is not a realistic, or desired approach due to the high storage + requirements, and the delays introduced when storing massive data. Accordingly, many synopses/sketches have been proposed that can summarize the stream + in small memory (usually sufficiently small to be stored in RAM), such that aggregate queries can be efficiently approximated, without storing the full + stream. However, past synopses predominantly focus on summarizing single-attribute streams, and cannot handle filters and constraints on arbitrary subsets + of multiple attributes efficiently. In this work, we propose OmniSketch, the first sketch that scales to fast-paced and complex data streams + (with many attributes), and supports aggregates with filters on multiple attributes, dynamically chosen at query time. The sketch offers probabilistic + guarantees, a favorable space-accuracy tradeoff, and a worst-case logarithmic complexity for updating and for query execution. We demonstrate experimentally + with both real and synthetic data that the sketch outperforms the state-of-the-art, and that it can approximate complex ad-hoc queries within the + configured accuracy guarantees, with small memory requirements. + +
Dandelion Hashtable: Beyond Billion In-memory Requests per Second on a Commodity Server. +
Antonios Katsarakis (Huawei Research)*; Vasilis Gavrielatos (Huawei); Nikos Ntarmos (Edinburgh Research Center, Central Software Institute, Huawei) +
Click to display the abstract + + This paper presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, + state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for + data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block + all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts + a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, + (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. + In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12×) the throughput of the state-of-the-art closed-addressing (open-addressing) + resizable hashtable on Gets (Deletes). + +
Proportionality on Spatial Data with Context +
Fakas, George; Kalamatianos, Georgios; + +
1:15-3:00pm Lunch break & Mentoring Event
3:00 - 4:30pm Updates/Highlights in DB
4:30 - 5:00pmAthena/Archimedes
30 min break Break
Session 3: "New directions in DB Panel" Chair: Chair
Session 3: "Panel AI in Academia / Education-Research-System Design"
5:30 - 6:30pm5:00 - 6:30pm Panel
6:306:30pm Closing remarks