197,241 research outputs found
From data towards knowledge: Revealing the architecture of signaling systems by unifying knowledge mining and data mining of systematic perturbation data
Genetic and pharmacological perturbation experiments, such as deleting a gene
and monitoring gene expression responses, are powerful tools for studying
cellular signal transduction pathways. However, it remains a challenge to
automatically derive knowledge of a cellular signaling system at a conceptual
level from systematic perturbation-response data. In this study, we explored a
framework that unifies knowledge mining and data mining approaches towards the
goal. The framework consists of the following automated processes: 1) applying
an ontology-driven knowledge mining approach to identify functional modules
among the genes responding to a perturbation in order to reveal potential
signals affected by the perturbation; 2) applying a graph-based data mining
approach to search for perturbations that affect a common signal with respect
to a functional module, and 3) revealing the architecture of a signaling system
organize signaling units into a hierarchy based on their relationships.
Applying this framework to a compendium of yeast perturbation-response data, we
have successfully recovered many well-known signal transduction pathways; in
addition, our analysis have led to many hypotheses regarding the yeast signal
transduction system; finally, our analysis automatically organized perturbed
genes as a graph reflecting the architect of the yeast signaling system.
Importantly, this framework transformed molecular findings from a gene level to
a conceptual level, which readily can be translated into computable knowledge
in the form of rules regarding the yeast signaling system, such as "if genes
involved in MAPK signaling are perturbed, genes involved in pheromone responses
will be differentially expressed"
An intelligent recommendation system framework for student relationship management
In order to enhance student satisfaction, many services have been provided in order to meet student needs. A recommendation system is a significant service which can be used to assist students in several ways. This paper proposes a conceptual framework of an Intelligent Recommendation System in order to support Student Relationship Management (SRM) for a Thai private university. This article proposed the system architecture of an Intelligent Recommendation System (IRS) which aims to assist students to choose an appropriate course for their studies. Moreover, this study intends to compare different data mining techniques in various recommendation systems and to determine appropriate algorithms for the proposed electronic Intelligent Recommendation System (IRS). The IRS also aims to support Student Relationship Management (SRM) in the university. The IRS has been designed using data mining and artificial intelligent techniques such as clustering, association rule and classification
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an
essential role in semantic search. Prior conceptual graph construction
approaches typically extract high-frequent, coarse-grained, and time-invariant
concepts from formal texts. In real applications, however, it is necessary to
extract less-frequent, fine-grained, and time-varying conceptual knowledge and
build taxonomy in an evolving manner. In this paper, we introduce an approach
to implementing and deploying the conceptual graph at Alibaba. Specifically, We
propose a framework called AliCG which is capable of a) extracting fine-grained
concepts by a novel bootstrapping with alignment consensus approach, b) mining
long-tail concepts with a novel low-resource phrase mining approach, c)
updating the graph dynamically via a concept distribution estimation method
based on implicit and explicit user behaviors. We have deployed the framework
at Alibaba UC Browser. Extensive offline evaluation as well as online A/B
testing demonstrate the efficacy of our approach.Comment: Accepted by KDD 2021 (Applied Data Science Track
Unified Approach in the DSS Development Process
The structure of today's decision support environment become very complex due to new generation of Business Intelligence applications and technologies like Data Warehouse, OLAP (On Line Analytical Processing) and Data Mining. In this respect DSS development process are not simple and needs an adequate methodology or framework able to manage different tools and platforms to achieve manager's requirements. The DSS development process must be view like a unified and iterative set of activities and operations. The new techniques based on Unified Process (UP) methodology and UML (Unified Modeling Language) it seems to be appropriate for DSS development using prototyping and RAD (Rapid Application Development) techniques. In this paper we present a conceptual framework for development and integrate Decision Support Systems using Unified Process Methodology and UML.Decision Support Systems, Unified Process, UML, Prototyping, DSS Tools
Fairness of Exposure in Rankings
Rankings are ubiquitous in the online world today. As we have transitioned
from finding books in libraries to ranking products, jobs, job applicants,
opinions and potential romantic partners, there is a substantial precedent that
ranking systems have a responsibility not only to their users but also to the
items being ranked. To address these often conflicting responsibilities, we
propose a conceptual and computational framework that allows the formulation of
fairness constraints on rankings in terms of exposure allocation. As part of
this framework, we develop efficient algorithms for finding rankings that
maximize the utility for the user while provably satisfying a specifiable
notion of fairness. Since fairness goals can be application specific, we show
how a broad range of fairness constraints can be implemented using our
framework, including forms of demographic parity, disparate treatment, and
disparate impact constraints. We illustrate the effect of these constraints by
providing empirical results on two ranking problems.Comment: In Proceedings of the 24th ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, London, UK, 201
Color Image Clustering using Block Truncation Algorithm
With the advancement in image capturing device, the image data been generated at high volume. If images are analyzed properly, they can reveal useful information to the human users. Content based image retrieval address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intraclass similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters
Information mining projects management process
Information Mining (also known as Knowledge Discovery Process) is a growing discipline in continuous expansion. Most of the progress accomplished, are focus on the development activities (i.e. those technical activities associated with the comprehension and adaptation of data, and the implementation of data mining algorithm). According to this conceptual framework, several process models were developed, which allow organizing and defining the set of tasks related to the development of information mining projects. These approaches omit the set of tasks oriented to the management and control of the process. In this paper, we propose a transversal management process to the development process currently in use in information mining projects. The proposed process focuses on removing existing gaps, providing an improvement on the project's maturity and quality levels.Instituto de Investigación en InformáticaFacultad de Informátic
Trajectory Data Analysis in Support of Understanding Movement Patterns: A Data Mining Approach
Recent developments in wireless technology, mobility and networking infrastructures increased the amounts of data being captured every second. Data captured from the digital traces of moving objects and devices is called trajectory data. With the increasing volume of spatiotemporal trajectories, constructive and meaningful knowledge needs to be extracted. In this paper, a conceptual framework is proposed to apply data mining techniques on trajectories and semantically enrich the extracted patterns. A design science research approach is followed, where the framework is tested and evaluated using a prototypical instantiation, built to support decisions in the context of the Egyptian tourism industry. By applying association rule mining, the revealed time-stamped frequently visited regions of interest (ROI) patterns show that specific semantic annotations are required at early stages in the process and on lower levels of detail, refuting the presumption of cross-application usable patterns
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