Using ECA rules to implement mobile query agents for fast-evolving pure P2P database systems

Using ECA rules to implement mobile query agents for fast-evolving pure P2P database system

Comportamento de Trichogramma pretiosum Riley (HYMENOPTERA: TRICHOGRAMMATIDAE) no manejo biológico de Spodoptera eridania (CRAMER) (LEPIDOPTERA: NOCTUIDAE), na cultura do tomateiro

A produção de olerícolas é de suma importância para a agricultura familiar, proporcionando renda em pequenas áreas de cultivos. O tomate é a olerícola fruto mais importante do Estado do Espírito Santo, estando cultivada em 2.508 ha de sua área. A quantidade de pragas que atacam esta cultura torna-a muito susceptível a prejuízos. Ultimamente Spodoptera eridania (Cramer) (Lep.: Noctuidae) tem sido relatada com frequência nos cultivos de tomate. Esta praga é capaz de se alimentar das folhas do tomateiro assim como seus frutos, tornando-os impróprios para o consumo, gerando prejuízos. Não existem produtos químicos registrados para a praga nesta cultura, forçando que sejam aplicados produtos não registrados para seu manejo, propiciando o surgimento de populações resistentes e sem a barreira natural que são os inimigos naturais (decorrente do uso indiscriminado de agrotóxicos de amplo espectro). No manejo Fitossanitário de Pragas (MFP), a utilização da liberação de parasitoides do gênero Trichogramma (Hym.: Trichogrammatidae) tem se tornado uma ferramenta viável, entretanto necessita de estudos aprofundados para a obtenção de sucesso. Desta maneira, o presente estudo objetivou realizar coletas, identificações morfológica e molecular de Trichogramma sp, e verificar o desempenho em ovos de S. eridania em laboratório e campo, na cultura do tomate. Foram realizadas 24 coletas de Trichogramma em diferentes regiões do estado do Espírito Santo, sendo positivas 9 coletas, todas pertencentes a espécie Trichogramma pretiosum (Riley). O parasitismo em ovos de S. eridania foi verificado para todas as linhagens coletadas, todavia, os valores médios observados diferiram, variando entre 10,37 a 68,50 (p 0,05). O local de oviposição observado diferiu entre si pelo teste de Chi-quadrado (p < 0,001), sendo a parte baixeira da planta a preferêncial. Foi realizado um experimento para saber o número ideal de T. pretiosum a ser liberado por hectare, onde foram liberados diferentes densidades do parasitoide, que parasitaram os ovos da praga por 24 horas. O número ideal de T. pretiosum por hectare na cultura do tomate para o manejo de S. eridania é de 500 mil/ha proporcionando um parasitismo de (13,43%)

Managing scientific data

Data-oriented scientific processes depend on fast, accurate analysis of experimental data generated through empirical observation and simulation. However, scientists are increasingly overwhelmed by the volume of data produced by their own experiments. With improving instrument precision and the complexity of the simulated models, data overload promises to only get worse. The inefficiency of existing database management systems (DBMSs) for addressing the requirements of scientists has led to many application-specific systems. Unlike their general-purpose counterparts, these systems require more resources, hindering reuse of knowledge. Still, the data-management community aspires to general-purpose scientific data management. Here, we explore the most important requirements of such systems and the techniques being used to address them

Multi-objective query optimization in Spark SQL

International audienc

An Economic Model for Self-tuned Cloud Caching

Cloud computing, the new trend for service infrastructures requires user multi-tenancy as well as minimal capital expenditure. In a cloud that services large amounts of data that are massively collected and queried, such as scientific data, users typically pay for query services. The cloud supports caching of data in order to provide quality query services. User payments cover query execution costs and maintenance of cloud infrastructure, and incur cloud profit. The challenge resides in providing efficient and resource-economic query services while maintaining a profitable cloud. In this work we propose an economic model for self-tuned cloud caching targeting the service of scientific data. The proposed economy is adapted to policies that encourage high-quality individual and overall query services but also brace the profit of the cloud. We propose a cost model that takes into account all possible query and infrastructure expenditure. The experimental study proves that the proposed solution is viable for a variety of workloads and data

Adaptive query execution for data management in the cloud

A major component of many cloud services is query processing on data stored in the underlying cloud cluster. The traditional techniques for query processing on a cluster are those offered by parallel DBMS. These techniques however, cannot guarantee high performance for cloud; parallel DBMS lack adequate fault tolerance mechanisms in order to deal with non-negligible software and hardware failures. MapReduce, on the other hand, allows query processing solutions that are fault tolerant, but imposes substantial overheads. In this paper, we propose an adaptive software architecture which can effortlessly switch between MapReduce and parallel DBMS in order to efficiently process queries regardless of their response times. Switching between the two architectures is performed in a transparent manner based on an intuitive cost model, which computes the expected execution time in presence of failures. The experimental results show that the adaptive architecture achieves the lowest possible query execution time for various scenarios

Optimal service pricing for a cloud cache

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource-economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental results prove the efficiency of the proposed solution