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ITMO University
Why we think this paper is great for you:
This paper directly addresses optimizing data warehouse performance using integer surrogate keys, which is highly relevant to your interest in data warehousing and efficient database design. It offers practical insights for improving your systems.
This paper directly addresses optimizing data warehouse performance using integer surrogate keys, which is highly relevant to your interest in data warehousing and efficient database design. It offers practical insights for improving your systems.
Abstract
The aim of this paper is to examine and demonstrate how integer-based datetime labels (integer surrogate keys for time) can optimize data-warehouse and time-series performance, proposing practical formats and algorithms and validating their efficiency on real-world workloads. It is shown that replacing standard DATE and TIMESTAMP types with 32- and 64-bit integer formats reduces storage requirements by 30-60 percent and speeds up query execution by 25-40 percent. The paper presents indexing, aggregation, compression, and batching algorithms demonstrating up to an eightfold increase in throughput. Practical examples from finance, telecommunications, IoT, and scientific research confirm the efficiency and versatility of the proposed approach.
The aim of this paper is to examine and demonstrate how integer-based datetime labels (integer surrogate keys for time) can optimize data-warehouse and time-series performance, proposing practical formats and algorithms and validating their efficiency on real-world workloads. It is shown that replacing standard DATE and TIMESTAMP types with 32- and 64-bit integer formats reduces storage requirements by 30-60 percent and speeds up query execution by 25-40 percent. The paper presents indexing, aggregation, compression, and batching algorithms demonstrating up to an eightfold increase in throughput. Practical examples from finance, telecommunications, IoT, and scientific research confirm the efficiency and versatility of the proposed approach.
AI Summary
- 64-bit Integer Format (YYYYMMDDHHMMSSXXXXX): A specific integer representation providing precision down to 100 microseconds, with a defined bit layout for year, month, day, hours, minutes, seconds, and fractional seconds. [3]
- Replacing standard DATE/TIMESTAMP types with 32-bit (YYYYMMDD) and 64-bit (YYYYMMDDHHMMSSXXXXX) integer formats reduces storage by 30-60% and accelerates query execution by 25-40%. [2]
- The proposed integer-based timestamp methodology, combined with optimized indexing, range search, aggregation, batching, and compression algorithms, can achieve up to an eightfold increase in system throughput. [2]
- Batching operations with integer timestamps is a critical optimization, improving throughput up to 8x and reducing network load up to 5x, with optimal batch sizes typically between 1,000-5,000 records for SSD-based systems. [2]
- International Atomic Time (TAI) provides a linear, high-precision, and anomaly-free time scale, making it ideal for integer-based timestamp storage in critical systems, though a hybrid TAI/UTC approach is recommended for most commercial applications. [2]
- Real-world applications in finance (HFT), telecommunications (CDR processing), and IoT (equipment monitoring) demonstrate substantial performance gains, including faster filtering, more efficient indexes, and simplified data partitioning. [2]
- Leading industry systems like Meta's Scuba, financial exchanges, ClickHouse, and Apache Druid already leverage integer-based time storage for high-performance, petabyte-scale data processing and sub-second query requirements. [2]
- Future research should prioritize developing unified storage/processing standards, formalizing hybrid TAI/UTC models, exploring hardware acceleration (GPU/FPGA), and enhancing query optimizer awareness for integer timestamps. [2]
- Integer Surrogate Keys for Time: Integer-based datetime labels (e.g., YYYYMMDD or YYYYMMDDHHMMSSXXXXX) used to represent temporal data, replacing native DATE/TIMESTAMP types. [2]
- 32-bit Integer Format (YYYYMMDD): A specific integer representation for dates, offering a 40-60% storage reduction compared to traditional DATE types. [2]
VietnameseGerman
Why we think this paper is great for you:
This paper explores natural language interaction with relational databases, directly aligning with your interest in relational databases and making querying more accessible than traditional SQL. You'll find its approach to LLM agents for database interaction particularly insightful.
This paper explores natural language interaction with relational databases, directly aligning with your interest in relational databases and making querying more accessible than traditional SQL. You'll find its approach to LLM agents for database interaction particularly insightful.
Abstract
Interacting with relational databases remains challenging for users across different expertise levels, particularly when composing complex analytical queries or performing administrative tasks. Existing systems typically address either natural language querying or narrow aspects of database administration, lacking a unified and intelligent interface for general-purpose database interaction. We introduce AskDB, a large language model powered agent designed to bridge this gap by supporting both data analysis and administrative operations over SQL databases through natural language. Built on Gemini 2, AskDB integrates two key innovations: a dynamic schema-aware prompting mechanism that effectively incorporates database metadata, and a task decomposition framework that enables the agent to plan and execute multi-step actions. These capabilities allow AskDB to autonomously debug derived SQL, retrieve contextual information via real-time web search, and adaptively refine its responses. We evaluate AskDB on a widely used Text-to-SQL benchmark and a curated set of DBA tasks, demonstrating strong performance in both analytical and administrative scenarios. Our results highlight the potential of AskDB as a unified and intelligent agent for relational database systems, offering an intuitive and accessible experience for end users.
Interacting with relational databases remains challenging for users across different expertise levels, particularly when composing complex analytical queries or performing administrative tasks. Existing systems typically address either natural language querying or narrow aspects of database administration, lacking a unified and intelligent interface for general-purpose database interaction. We introduce AskDB, a large language model powered agent designed to bridge this gap by supporting both data analysis and administrative operations over SQL databases through natural language. Built on Gemini 2, AskDB integrates two key innovations: a dynamic schema-aware prompting mechanism that effectively incorporates database metadata, and a task decomposition framework that enables the agent to plan and execute multi-step actions. These capabilities allow AskDB to autonomously debug derived SQL, retrieve contextual information via real-time web search, and adaptively refine its responses. We evaluate AskDB on a widely used Text-to-SQL benchmark and a curated set of DBA tasks, demonstrating strong performance in both analytical and administrative scenarios. Our results highlight the potential of AskDB as a unified and intelligent agent for relational database systems, offering an intuitive and accessible experience for end users.
Portland State University
Why we think this paper is great for you:
Focusing on fine-grained access control in relational database management systems, this paper is crucial for your understanding of secure and scalable database design. It addresses practical challenges in managing access policies within relational databases.
Focusing on fine-grained access control in relational database management systems, this paper is crucial for your understanding of secure and scalable database design. It addresses practical challenges in managing access policies within relational databases.
Abstract
The proliferation of smart technologies and evolving privacy regulations such as the GDPR and CPRA has increased the need to manage fine-grained access control (FGAC) policies in database management systems (DBMSs). Existing approaches to enforcing FGAC policies do not scale to thousands of policies, leading to degraded query performance and reduced system effectiveness. We present Sieve, a middleware for relational DBMSs that combines query rewriting and caching to optimize FGAC policy enforcement. Sieve rewrites a query with guarded expressions that group and filter policies and can efficiently use indexes in the DBMS. It also integrates a caching mechanism with an effective replacement strategy and a refresh mechanism to adapt to dynamic workloads. Experiments on two DBMSs with real and synthetic datasets show that Sieve scales to large datasets and policy corpora, maintaining low query latency and system load and improving policy evaluation performance by between 2x and 10x on workloads with 200 to 1,200 policies. The caching extension further improves query performance by between 6 and 22 percent under dynamic workloads, especially with larger cache sizes. These results highlight Sieve's applicability for real-time access control in smart environments and its support for efficient, scalable management of user preferences and privacy policies.
The proliferation of smart technologies and evolving privacy regulations such as the GDPR and CPRA has increased the need to manage fine-grained access control (FGAC) policies in database management systems (DBMSs). Existing approaches to enforcing FGAC policies do not scale to thousands of policies, leading to degraded query performance and reduced system effectiveness. We present Sieve, a middleware for relational DBMSs that combines query rewriting and caching to optimize FGAC policy enforcement. Sieve rewrites a query with guarded expressions that group and filter policies and can efficiently use indexes in the DBMS. It also integrates a caching mechanism with an effective replacement strategy and a refresh mechanism to adapt to dynamic workloads. Experiments on two DBMSs with real and synthetic datasets show that Sieve scales to large datasets and policy corpora, maintaining low query latency and system load and improving policy evaluation performance by between 2x and 10x on workloads with 200 to 1,200 policies. The caching extension further improves query performance by between 6 and 22 percent under dynamic workloads, especially with larger cache sizes. These results highlight Sieve's applicability for real-time access control in smart environments and its support for efficient, scalable management of user preferences and privacy policies.
New York University
Why we think this paper is great for you:
This paper delves into natural language interfaces for databases, offering insights into user perspectives on moving beyond formal SQL queries. It's highly relevant to your interest in database usability and design.
This paper delves into natural language interfaces for databases, offering insights into user perspectives on moving beyond formal SQL queries. It's highly relevant to your interest in database usability and design.
Abstract
Natural Language Interfaces for Databases (NLIDBs) aim to make database querying accessible by allowing users to ask questions in everyday language rather than using formal SQL queries. Despite significant advancements in translation accuracy, critical usability challenges, such as user frustration, query refinement strategies, and error recovery, remain underexplored. To investigate these usability dimensions, we conducted a mixed-method user study comparing SQL-LLM, a state-of-the-art NL2SQL system, with Snowflake, a traditional SQL analytics platform. Our controlled evaluation involved 20 participants completing realistic database querying tasks across 12 queries each. Results show that SQL-LLM significantly reduced query completion times by 10 to 30 percent (mean: 418 s vs. 629 s, p = 0.036) and improved overall accuracy from 50 to 75 percent (p = 0.002). Additionally, participants using SQL-LLM exhibited fewer query reformulations, recovered from errors 30 to 40 seconds faster, and reported lower frustration levels compared to Snowflake users. Behavioral analysis revealed that SQL-LLM encouraged structured, schema-first querying strategies, enhancing user confidence and efficiency, particularly for complex queries. These findings underscore the practical significance of well-designed, user-friendly NLIDBs in business analytics settings, emphasizing the critical role of usability alongside technical accuracy in real-world deployments.
Natural Language Interfaces for Databases (NLIDBs) aim to make database querying accessible by allowing users to ask questions in everyday language rather than using formal SQL queries. Despite significant advancements in translation accuracy, critical usability challenges, such as user frustration, query refinement strategies, and error recovery, remain underexplored. To investigate these usability dimensions, we conducted a mixed-method user study comparing SQL-LLM, a state-of-the-art NL2SQL system, with Snowflake, a traditional SQL analytics platform. Our controlled evaluation involved 20 participants completing realistic database querying tasks across 12 queries each. Results show that SQL-LLM significantly reduced query completion times by 10 to 30 percent (mean: 418 s vs. 629 s, p = 0.036) and improved overall accuracy from 50 to 75 percent (p = 0.002). Additionally, participants using SQL-LLM exhibited fewer query reformulations, recovered from errors 30 to 40 seconds faster, and reported lower frustration levels compared to Snowflake users. Behavioral analysis revealed that SQL-LLM encouraged structured, schema-first querying strategies, enhancing user confidence and efficiency, particularly for complex queries. These findings underscore the practical significance of well-designed, user-friendly NLIDBs in business analytics settings, emphasizing the critical role of usability alongside technical accuracy in real-world deployments.
Maastricht University
Why we think this paper is great for you:
This paper introduces topological data analysis for uncovering patterns in business datasets, providing a unique analytical perspective on data that could complement your data warehousing knowledge. It offers a different lens for understanding complex data structures.
This paper introduces topological data analysis for uncovering patterns in business datasets, providing a unique analytical perspective on data that could complement your data warehousing knowledge. It offers a different lens for understanding complex data structures.
Abstract
Modern business and economic datasets often exhibit nonlinear, multi-scale structures that traditional linear tools under-represent. Topological Data Analysis (TDA) offers a geometric lens for uncovering robust patterns, such as connected components, loops and voids, across scales. This paper provides an intuitive, figure-driven introduction to persistent homology and a practical, reproducible TDA pipeline for applied analysts. Through comparative case studies in consumer behavior, equity markets (SAX/eSAX vs.\ TDA) and foreign exchange dynamics, we demonstrate how topological features can reveal segmentation patterns and structural relationships beyond classical statistical methods. We discuss methodological choices regarding distance metrics, complex construction and interpretation, and we introduce the \textit{Topological Stability Index} (TSI), a simple yet interpretable indicator of structural variability derived from persistence lifetimes. We conclude with practical guidelines for TDA implementation, visualization and communication in business and economic analytics.
Modern business and economic datasets often exhibit nonlinear, multi-scale structures that traditional linear tools under-represent. Topological Data Analysis (TDA) offers a geometric lens for uncovering robust patterns, such as connected components, loops and voids, across scales. This paper provides an intuitive, figure-driven introduction to persistent homology and a practical, reproducible TDA pipeline for applied analysts. Through comparative case studies in consumer behavior, equity markets (SAX/eSAX vs.\ TDA) and foreign exchange dynamics, we demonstrate how topological features can reveal segmentation patterns and structural relationships beyond classical statistical methods. We discuss methodological choices regarding distance metrics, complex construction and interpretation, and we introduce the \textit{Topological Stability Index} (TSI), a simple yet interpretable indicator of structural variability derived from persistence lifetimes. We conclude with practical guidelines for TDA implementation, visualization and communication in business and economic analytics.
Oakland University
Why we think this paper is great for you:
While highly theoretical, this paper discusses constructing a database for classifying mathematical structures, which might offer a very abstract view on data organization. Its primary focus is on advanced mathematics rather than practical database systems.
While highly theoretical, this paper discusses constructing a database for classifying mathematical structures, which might offer a very abstract view on data organization. Its primary focus is on advanced mathematics rather than practical database systems.
Abstract
We develop a computational framework for classifying Galois groups of irreducible degree-7 polynomials over~$\mathbb{Q}$, combining explicit resolvent methods with machine learning techniques. A database of over one million normalized projective septics is constructed, each annotated with algebraic invariants~$J_0, \dots, J_4$ derived from binary transvections. For each polynomial, we compute resolvent factorizations to determine its Galois group among the seven transitive subgroups of~$S_7$ identified by Foulkes. Using this dataset, we train a neurosymbolic classifier that integrates invariant-theoretic features with supervised learning, yielding improved accuracy in detecting rare solvable groups compared to coefficient-based models. The resulting database provides a reproducible resource for constructive Galois theory and supports empirical investigations into group distribution under height constraints. The methodology extends to higher-degree cases and illustrates the utility of hybrid symbolic-numeric techniques in computational algebra.
We develop a computational framework for classifying Galois groups of irreducible degree-7 polynomials over~$\mathbb{Q}$, combining explicit resolvent methods with machine learning techniques. A database of over one million normalized projective septics is constructed, each annotated with algebraic invariants~$J_0, \dots, J_4$ derived from binary transvections. For each polynomial, we compute resolvent factorizations to determine its Galois group among the seven transitive subgroups of~$S_7$ identified by Foulkes. Using this dataset, we train a neurosymbolic classifier that integrates invariant-theoretic features with supervised learning, yielding improved accuracy in detecting rare solvable groups compared to coefficient-based models. The resulting database provides a reproducible resource for constructive Galois theory and supports empirical investigations into group distribution under height constraints. The methodology extends to higher-degree cases and illustrates the utility of hybrid symbolic-numeric techniques in computational algebra.
Oakland University
Why we think this paper is great for you:
This paper explores the use of databases for highly specialized mathematical classification, providing a very niche perspective on data storage. It is primarily focused on theoretical algebra rather than general database applications.
This paper explores the use of databases for highly specialized mathematical classification, providing a very niche perspective on data storage. It is primarily focused on theoretical algebra rather than general database applications.
Abstract
We develop a computational framework for classifying Galois groups of irreducible degree-7 polynomials over~$\mathbb{Q}$, combining explicit resolvent methods with machine learning techniques. A database of over one million normalized projective septics is constructed, each annotated with algebraic invariants~$J_0, \dots, J_4$ derived from binary transvections. For each polynomial, we compute resolvent factorizations to determine its Galois group among the seven transitive subgroups of~$S_7$ identified by Foulkes. Using this dataset, we train a neurosymbolic classifier that integrates invariant-theoretic features with supervised learning, yielding improved accuracy in detecting rare solvable groups compared to coefficient-based models. The resulting database provides a reproducible resource for constructive Galois theory and supports empirical investigations into group distribution under height constraints. The methodology extends to higher-degree cases and illustrates the utility of hybrid symbolic-numeric techniques in computational algebra.
We develop a computational framework for classifying Galois groups of irreducible degree-7 polynomials over~$\mathbb{Q}$, combining explicit resolvent methods with machine learning techniques. A database of over one million normalized projective septics is constructed, each annotated with algebraic invariants~$J_0, \dots, J_4$ derived from binary transvections. For each polynomial, we compute resolvent factorizations to determine its Galois group among the seven transitive subgroups of~$S_7$ identified by Foulkes. Using this dataset, we train a neurosymbolic classifier that integrates invariant-theoretic features with supervised learning, yielding improved accuracy in detecting rare solvable groups compared to coefficient-based models. The resulting database provides a reproducible resource for constructive Galois theory and supports empirical investigations into group distribution under height constraints. The methodology extends to higher-degree cases and illustrates the utility of hybrid symbolic-numeric techniques in computational algebra.
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