Related papers: Diamond Dicing
A set of preferred records can be obtained from a large database in a multi-criteria setting using various computational methods which either depend on the concept of dominance or on the concept of utility or scoring function based on the…
This paper studies the complexity of query evaluation for databases whose relations are partially ordered; the problem commonly arises when combining or transforming ordered data from multiple sources. We focus on queries in a useful…
Electronic commerce is emerging as the killer domain for data mining technology. The following are five desiderata for success. Seldom are they they all present in one data mining application. 1. Data with rich descriptions. For example,…
We consider the problem of aligning a pair of databases with correlated entries. We introduce a new measure of correlation in a joint distribution that we call cycle mutual information. This measure has operational significance: it…
Data distillation is the problem of reducing the volume oftraining data while keeping only the necessary information. With thispaper, we deeper explore the new data distillation algorithm, previouslydesigned for image data. Our experiments…
Distance Comparison Operations (DCOs), which decide whether the distance between a data vector and a query is within a threshold, are a critical performance bottleneck in vector similarity search. Recent DCO methods that avoid…
In the age of big data, sorting is an indispensable operation for DBMSes and similar systems. Having data sorted can help produce query plans with significantly lower run times. It also can provide other benefits like having non-blocking…
Data mining is a new concept & an exploration and analysis of large data sets, in order to discover meaningful patterns and rules. Many organizations are now using the data mining techniques to find out meaningful patterns from the…
One of the challenges currently problems in the use of cloud services is the task of designing of specialized data management systems. This is especially important for hybrid systems in which the data are located in public and private…
Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties. In order to protect organizations database DAM systems have two main roles, monitoring…
Machine learning (ML) models are powerful tools for detecting complex patterns within data, yet their "black box" nature limits their interpretability, hindering their use in critical domains like healthcare and finance. To address this…
Data organization is a difficult and essential component in cultural heritage applications. Over the years, a great amount of archaeological ceramic data have been created and processed by various methods and devices. Such ceramic data are…
Current architectures for main-memory online transaction processing (OLTP) database management systems (DBMS) typically use random scheduling to assign transactions to threads. This approach achieves uniform load across threads but it…
Database workloads are increasingly nesting artificial intelligence (AI) and machine learning (ML) pipelines and AI/ML model inferences with data processing, yielding hybrid SQL+AI/ML queries that mix relational operators with expensive,…
A central push in operations models over the last decade has been the incorporation of models of customer choice. Real world implementations of many of these models face the formidable stumbling block of simply identifying the `right' model…
Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. In this work we present Corral,…
To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging…
Consistent query answering over a database that violates primary key constraints is a classical hard problem in database research that has been traditionally dealt with logic programming. However, the applicability of existing logic-based…
Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to…
In 2010, the concept of data lake emerged as an alternative to data warehouses for big data management. Data lakes follow a schema-on-read approach to provide rich and flexible analyses. However, although trendy in both the industry and…