A systematic review of data quality issues in knowledge discovery tasks

Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

A Change Support Model for Distributed Collaborative Work

Distributed collaborative software development tends to make artifacts and decisions inconsistent and uncertain. We try to solve this problem by providing an information repository to reflect the state of works precisely, by managing the states of artifacts/products made through collaborative work, and the states of decisions made through communications. In this paper, we propose models and a tool to construct the artifact-related part of the information repository, and explain the way to use the repository to resolve inconsistencies caused by concurrent changes of artifacts. We first show the model and the tool to generate the dependency relationships among UML model elements as content of the information repository. Next, we present the model and the method to generate change support workflows from the information repository. These workflows give us the way to efficiently modify the change-related artifacts for each change request. Finally, we define inconsistency patterns that enable us to be aware of the possibility of inconsistency occurrences. By combining this mechanism with version control systems, we can make changes safely. Our models and tool are useful in the maintenance phase to perform changes safely and efficiently.Comment: 10 pages, 13 figures, 4 table

Quantum Non-Objectivity from Performativity of Quantum Phenomena

We analyze the logical foundations of quantum mechanics (QM) by stressing non-objectivity of quantum observables which is a consequence of the absence of logical atoms in QM. We argue that the matter of quantum non-objectivity is that, on the one hand, the formalism of QM constructed as a mathematical theory is self-consistent, but, on the other hand, quantum phenomena as results of experimenter's performances are not self-consistent. This self-inconsistency is an effect of that the language of QM differs much from the language of human performances. The first is the language of a mathematical theory which uses some Aristotelian and Russellian assumptions (e.g., the assumption that there are logical atoms). The second language consists of performative propositions which are self-inconsistent only from the viewpoint of conventional mathematical theory, but they satisfy another logic which is non-Aristotelian. Hence, the representation of quantum reality in linguistic terms may be different: from a mathematical theory to a logic of performative propositions. To solve quantum self-inconsistency, we apply the formalism of non-classical self-referent logics

Trust-based security for the OLSR routing protocol

International audienceThe trust is always present implicitly in the protocols based on cooperation, in particular, between the entities involved in routing operations in Ad hoc networks. Indeed, as the wireless range of such nodes is limited, the nodes mutually cooperate with their neighbors in order to extend the remote nodes and the entire network. In our work, we are interested by trust as security solution for OLSR protocol. This approach fits particularly with characteristics of ad hoc networks. Moreover, the explicit trust management allows entities to reason with and about trust, and to take decisions regarding other entities. In this paper, we detail the techniques and the contributions in trust-based security in OLSR. We present trust-based analysis of the OLSR protocol using trust specification language, and we show how trust-based reasoning can allow each node to evaluate the behavior of the other nodes. After the detection of misbehaving nodes, we propose solutions of prevention and countermeasures to resolve the situations of inconsistency, and counter the malicious nodes. We demonstrate the effectiveness of our solution taking different simulated attacks scenarios. Our approach brings few modifications and is still compatible with the bare OLSR

Semantics-based selection of everyday concepts in visual lifelogging

Concept-based indexing, based on identifying various semantic concepts appearing in multimedia, is an attractive option for multimedia retrieval and much research tries to bridge the semantic gap between the media’s low-level features and high-level semantics. Research into concept-based multimedia retrieval has generally focused on detecting concepts from high quality media such as broadcast TV or movies, but it is not well addressed in other domains like lifelogging where the original data is captured with poorer quality. We argue that in noisy domains such as lifelogging, the management of data needs to include semantic reasoning in order to deduce a set of concepts to represent lifelog content for applications like searching, browsing or summarisation. Using semantic concepts to manage lifelog data relies on the fusion of automatically-detected concepts to provide a better understanding of the lifelog data. In this paper, we investigate the selection of semantic concepts for lifelogging which includes reasoning on semantic networks using a density-based approach. In a series of experiments we compare different semantic reasoning approaches and the experimental evaluations we report on lifelog data show the efficacy of our approach

Method For Detecting Shilling Attacks In E-commerce Systems Using Weighted Temporal Rules

The problem of shilling attacks detecting in e-commerce systems is considered. The purpose of such attacks is to artificially change the rating of individual goods or services by users in order to increase their sales. A method for detecting shilling attacks based on a comparison of weighted temporal rules for the processes of selecting objects with explicit and implicit feedback from users is proposed. Implicit dependencies are specified through the purchase of goods and services. Explicit feedback is formed through the ratings of these products. The temporal rules are used to describe hidden relationships between the choices of user groups at two consecutive time intervals. The method includes the construction of temporal rules for explicit and implicit feedback, their comparison, as well as the formation of an ordered subset of temporal rules that capture potential shilling attacks. The method imposes restrictions on the input data on sales and ratings, which must be ordered by time or have timestamps. This method can be used in combination with other approaches to detecting shilling attacks. Integration of approaches allows to refine and supplement the existing attack patterns, taking into account the latest changes in user priorities