10,920 research outputs found
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
Complexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity\ud
This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term “complexity”. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of “true” complex phenomena.\u
An empirical study on how humans appreciate automated counterfactual explanations which embrace imprecise information
The explanatory capacity of interpretable fuzzy rule-based classifiers is usually limited to offering explanations for the predicted class only. A lack of potentially useful explanations for non-predicted alternatives can be overcome by designing methods for the so-called counterfactual reasoning. Nevertheless, state-of-the-art methods for counterfactual explanation generation require special attention to human evaluation aspects, as the final decision upon the classification under consideration is left for the end user. In this paper, we first introduce novel methods for qualitative and quantitative counterfactual explanation generation. Then, we carry out a comparative analysis of qualitative explanation generation methods operating on (combinations of) linguistic terms as well as a quantitative method suggesting precise changes in feature values. Then, we propose a new metric for assessing the perceived complexity of the generated explanations. Further, we design and carry out two human evaluation experiments to assess the explanatory power of the aforementioned methods. As a major result, we show that the estimated explanation complexity correlates well with the informativeness, relevance, and readability of explanations perceived by the targeted study participants. This fact opens the door to using the new automatic complexity metric for guiding multi-objective evolutionary explainable fuzzy modeling in the near futureIlia Stepin is an FPI researcher (grant PRE2019-090153). Jose M. Alonso-Moral is a Ramon y Cajal researcher (grant RYC-2016–19802). This work was supported by the Spanish Ministry of Science and Innovation (grants RTI2018-099646-B-I00, PID2021-123152OB-C21, and TED2021-130295B-C33) and the Galician Ministry of Culture, Education, Professional Training and University (grants ED431F2018/02, ED431G2019/04, and ED431C2022/19). All the grants were co-funded by the European Regional Development Fund (ERDF/FEDER program).S
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