A unified framework for indexing in database systems

Tsatalos, Odysseas G.; Ioannidis, Yannis

doi:10.1007/3-540-58435-8_183

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…These characteristics then provide a consistent and cost-effective data analysis platform for auditing and optimization across data mining and artificial intelligence (AI) approaches to obtain more detailed information on subsurface conditions. As a result, the unified database facilitates collaborations between geoengineers and stakeholders for better communication and promoting more efficient workflows [ 31 , 32 , 33 ]. Such analysis will then greatly help the geoengineers to identify patterns, and trends and anomalies that allow error elimination to be more based on informed decision-making and operational improvement [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

Abbaszadeh Shahri,

Shan,

Larsson

et al. 2024

Sensors

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In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

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Section: Introductionmentioning

confidence: 99%

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

Abbaszadeh Shahri,

Shan,

Larsson

et al. 2024

Sensors

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The GMAP: a versatile tool for physical data independence

Tsatalos

Solomon

Ioannidis

1996

The VLDB Journal The International Journal on Very Large Data B

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The next 50 years in database indexing or

2021

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Index structures are a building block of query processing and computer science in general. Since the dawn of computer technology there have been index structures. And since then, a myriad of index structures are being invented and published each and every year. In this paper we argue that the very idea of "inventing an index" is a misleading concept in the first place. It is the analogue of "inventing a physical query plan". This paper is a paradigm shift in which we propose to drop the idea to handcraft index structures (as done for binary search trees over B-trees to any form of learned index) altogether. We present a new automatic index breeding framework coined Genetic Generic Generation of Index Structures (GENE) . It is based on the observation that almost all index structures are assembled along three principal dimensions: (1) structural building blocks, e.g., a B-tree is assembled from two different structural node types (inner and leaf nodes), (2) a couple of invariants, e.g., for a B-tree all paths have the same length, and (3) decisions on the internal layout of nodes (row or column layout, etc.). We propose a generic indexing framework that can mimic many existing index structures along those dimensions. Based on that framework we propose a generic genetic index generation algorithm that, given a workload and an optimization goal, can automatically assemble and mutate, in other words 'breed' new index structure 'species'. In our experiments we follow multiple goals. We reexamine some good old wisdom from database technology. Given a specific workload, will GENE even breed an index that is equivalent to what our textbooks and papers currently recommend for such a workload? Or can we do even more? Our initial results strongly indicate that generated indexes are the next step in designing index structures.

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A unified framework for indexing in database systems

Cited by 3 publications

References 10 publications

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

The GMAP: a versatile tool for physical data independence

The next 50 years in database indexing or

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