2,349 research outputs found
Using Fuzzy Linguistic Representations to Provide Explanatory Semantics for Data Warehouses
A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses
Relational Algebra for In-Database Process Mining
The execution logs that are used for process mining in practice are often
obtained by querying an operational database and storing the result in a flat
file. Consequently, the data processing power of the database system cannot be
used anymore for this information, leading to constrained flexibility in the
definition of mining patterns and limited execution performance in mining large
logs. Enabling process mining directly on a database - instead of via
intermediate storage in a flat file - therefore provides additional flexibility
and efficiency. To help facilitate this ideal of in-database process mining,
this paper formally defines a database operator that extracts the 'directly
follows' relation from an operational database. This operator can both be used
to do in-database process mining and to flexibly evaluate process mining
related queries, such as: "which employee most frequently changes the 'amount'
attribute of a case from one task to the next". We define the operator using
the well-known relational algebra that forms the formal underpinning of
relational databases. We formally prove equivalence properties of the operator
that are useful for query optimization and present time-complexity properties
of the operator. By doing so this paper formally defines the necessary
relational algebraic elements of a 'directly follows' operator, which are
required for implementation of such an operator in a DBMS
Parameterized simplification logic I: reasoning with implications and classes of closure operators
Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/5399?template=romeoIn this paper, we present a general inference system for reasoning with if-then rules. They are defined using general lattice-theoretic notions and their semantics is defined using particular closure operators parameterized by systems of isotone Galois connections. In this general setting, we introduce a simplification logic, show its sound and complete axiomatization, and deal with related issues. The presented results can be seen as forming parameterized framework for dealing with if-then rules that allows to focus on particular dependencies obtained by choices of parameterizations.Supported by Grant TIN2017-89023-P of the Science and Innovation Ministry of Spain, which
is co-financed by the European Regional Development Fund (ERDF). V. Vychodil was also
supported the ECOP (Education for Competitiveness Operational Programme) project no.
CZ.1.07/2.3.00/20.0059, which was co-financed by the European Social Fund and the state budget
of the Czech Republic during 2011–2014
AsterixDB: A Scalable, Open Source BDMS
AsterixDB is a new, full-function BDMS (Big Data Management System) with a
feature set that distinguishes it from other platforms in today's open source
Big Data ecosystem. Its features make it well-suited to applications like web
data warehousing, social data storage and analysis, and other use cases related
to Big Data. AsterixDB has a flexible NoSQL style data model; a query language
that supports a wide range of queries; a scalable runtime; partitioned,
LSM-based data storage and indexing (including B+-tree, R-tree, and text
indexes); support for external as well as natively stored data; a rich set of
built-in types; support for fuzzy, spatial, and temporal types and queries; a
built-in notion of data feeds for ingestion of data; and transaction support
akin to that of a NoSQL store.
Development of AsterixDB began in 2009 and led to a mid-2013 initial open
source release. This paper is the first complete description of the resulting
open source AsterixDB system. Covered herein are the system's data model, its
query language, and its software architecture. Also included are a summary of
the current status of the project and a first glimpse into how AsterixDB
performs when compared to alternative technologies, including a parallel
relational DBMS, a popular NoSQL store, and a popular Hadoop-based SQL data
analytics platform, for things that both technologies can do. Also included is
a brief description of some initial trials that the system has undergone and
the lessons learned (and plans laid) based on those early "customer"
engagements
- …