National Institute of Standards and Technology における超伝導研究及び生活

A privacy preserving association rule mining algorithm was introducedThis algorithm preserved privacy of individual values by computing scalar productMeanwhile the algorithm of computing scalar product was given and the security was analyzed

Privacy preserving association rule mining in vertically partitioned data

Spinnaker is an experimental datastore that is designed to run on a large cluster of commodity servers in a single datacenter. It features key-based range partitioning, 3-way replication, and a transactional get-put API with the option to choose either strong or timeline consistency on reads. This paper describes Spinnaker's Paxos-based replication protocol. The use of Paxos ensures that a data partition in Spinnaker will be available for reads and writes as long a majority of its replicas are alive. Unlike traditional master-slave replication, this is true regardless of the failure sequence that occurs. We show that Paxos replication can be competitive with alternatives that provide weaker consistency guarantees. Compared to an eventually consistent datastore, we show that Spinnaker can be as fast or even faster on reads and only 5% to 10% slower on writes.

Using Paxos to Build a Scalable, Consistent, and Highly Available Datastore

The mechanisms of action of flavonoids in the brain: Direct versus indirect effects.

Incremental load is an important factor for successful data warehousing. Lack of standardized incremental refresh methodologies can lead to poor analytical results, which can be unacceptable to an organization's analytical community. Successful data warehouse implementation depends on consistent metadata as well as incremental data load techniques. If consistent load timestamps are maintained and efficient transformation algorithms are used, it is possible to refresh databases with complete accuracy and with little or no manual checking. This paper proposes an Extract-Transform-Load ETL metadata model that archives load observation timestamps and other useful load parameters. The author also recommends algorithms and techniques for incremental refreshes that enable table loading while ensuring data consistency, integrity, and improving load performance. In addition to significantly improving quality in incremental load techniques, these methods will save a substantial amount of data warehouse systems resources.

Incremental Load in a Data Warehousing Environment

A data warehouse provides information for analytical processing, decision making and data mining tools. As the concept of real-time enterprise evolves, the synchronism between transactional data and data warehouses, statically implemented, has been redefined. Traditional data warehouse systems have static structures of their schemas and relationships between data, and therefore are not able to support any dynamics in their structure and content. Their data is only periodically updated because they are not prepared for continuous data integration. For real-time enterprises with needs in decision support purposes, real-time data warehouses seem to be very promising. In this paper we present a methodology on how to adapt data warehouse schemas and user-end OLAP queries for efficiently supporting real-time data integration. To accomplish this, we use techniques such as table structure replication and query predicate restrictions for selecting data, to enable continuously loading data in the data warehouse with minimum impact in query execution time. We demonstrate the efficiency of the method by analyzing its impact in query performance using benchmark TPC-H executing query workloads while simultaneously performing continuous data integration at various insertion time rates.

Real-time data warehouse loading methodology

The purpose of a data warehouse is to aid decision making. As the real-time enterprise evolves, synchronism between transactional data and data warehouses is redefined. To cope with real-time requirements, the data warehouses must be able to enable continuous data integration, in order to deal with the most recent business data. Traditional data warehouses are unable to support any dynamics in structure and content while they are available for OLAP. Their data is periodically updated because they are unprepared for continuous data integration. For real-time enterprises with needs in decision support while the transactions are occurring, (near) real-time data warehousing seem very promising. In this paper we present a survey on testing today's most used loading techniques and analyze which are the best data loading methods, presenting a methodology for efficiently supporting continuous data integration for data warehouses. To accomplish this, we use techniques such as table structure replication with minimum content and query predicate restrictions for selecting data, to enable loading data in the data warehouse continuously, with minimum impact in query execution time. We demonstrate the efficiency of the method using benchmark TPC-H and executing query workloads while simultaneously performing continuous data integration.

Optimizing data warehouse loading procedures for enabling useful-time data warehousing

Data Warehouses (DWs) are the enterprise's most valuable asset in what concerns critical business information, making them an appealing target for attackers. Packaged database encryption solutions are considered the best solution to protect sensitive data. However, given the volume of data typically processed by DW queries, the existing encryption solutions heavily increase storage space and introduce very large overheads in query response time, due to decryption costs. In many cases, this performance degradation makes encryption unfeasible for use in DWs. In this paper we propose a transparent data masking solution for numerical values in DWs based on the mathematical modulus operator, which can be used without changing user application and DBMS source code. Our solution provides strong data security while introducing small overheads in both storage space and database performance. Several experimental evaluations using the TPC-H decision support benchmark and a real-world DW are included. The results show the overall efficiency of our proposal, demonstrating that it is a valid alternative to existing standard encryption routines for enforcing data confidentiality in DWs.

A data masking technique for data warehouses

Nitric oxide (NO) is a labile endogenous free radical produced upon glutamatergic neuronal activity in hippocampus by neuronal nitric oxide synthase (nNOS), where it acts as a modulator of both synaptic plasticity and cell death associated with neurodegen- eration. The low CNS levels and fast time dynamics of this molecule require the use of rapid analytical methods that can more accurately describe its signaling in vivo. This is critical for understanding how the kinetics of NO-dependent signaling pathways is translated into physio- logical or pathological functions. In these studies, we used NO selec- tive microelectrodes coupled with rapid electrochemical recording techniques to characterize for the first time the concentration dynam- ics of NO endogenously produced in hippocampus in vivo following activation of ionotropic glutamate receptors. Both L-glutamate (1-100 mM) and N-methyl-D-aspartate (NMDA; 0.01-5 mM) produced tran- sient, dose-dependent increases in extracellular NO concentration. The production of NO in the hippocampus by glutamate was decreased by the nNOS inhibitor 7-NI. Intraperitoneal administration of the NMDA receptor blocker, MK-801, and the inhibitor of a-amino- 3-hydroxy-5-methyl-4-isoazolepropionic acid (AMPA) receptor, NBQX, applied locally greatly attenuated glutamate-evoked overflow of NO. Thus, NO overflow elicited by activation of glutamate receptors appeared to result from an integrated activation of ionotropic gluta- mate receptors, both of the NMDA and AMPA receptors subtypes. Additionally, distinct concentration dynamics was observed in the tri- synaptic loop with stronger and longer lasting effects of glutamate activation on NO overflow seen in the CA1 region as compared with the dentate gyrus. Overall, the results provide a quantitative and tem- poral basis for a better understanding of NO activity in the rat hippo- campus. V C 2010 Wiley-Liss, Inc.

In Vivo Modulation of Nitric Oxide Concentration Dynamics Upon Glutamatergic Neuronal Activation in the Hippocampus

Data Warehouses (DWs) are the enterprise's most valuable assets in what concerns critical business information, making them an appealing target for malicious inside and outside attackers. Given the volume of data and the nature of DW queries, most of the existing data security solutions for databases are inefficient, consuming too many resources and introducing too much overhead in query response time, or resulting in too many false positive alarms (i.e., incorrect detection of attacks) to be checked. In this paper, we present a survey on currently available data security techniques, focusing on specific issues and requirements concerning their use in data warehousing environments. We also point out challenges and opportunities for future research work in this field.

Ricardo Jorge Santos

Papers

Real-time data warehouse loading methodology

Optimizing data warehouse loading procedures for enabling useful-time data warehousing

A data masking technique for data warehouses

In Vivo Modulation of Nitric Oxide Concentration Dynamics Upon Glutamatergic Neuronal Activation in the Hippocampus

A survey on data security in data warehousing: Issues, challenges and opportunities