The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we address the problem of data confidentiality in big data analytics. In many fields, much useful patterns can be extracted by applying machine learning techniques to big data. However, data confidentiality must be protected. In many scenarios, data confidentiality could well be a prerequisite for data to be shared. We present a scheme to provide provable secure data confidentiality...
Many IoT applications ingest and process time series data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity). To design and test such systems, it is desirable to have a high-performance traffic generator specifically designed for time series data, preferably using archived data to create a truly realistic workload. However, most existing traffic generator tools either are designed...
Distributed transaction can enable not only large-scale transacting business in highly scalable datastores, but also fast information extraction from big data through materialized view maintenance and incremental processing. Atomic commitment is key to the correctness of transaction processing. While two-phase commit (2PC) is widely used for distributed transaction commitment even in modern large-scale...
We present a transaction model which simultaneously supports different consistency levels, which include serial-izable transactions for strong consistency, and weaker consistency models such as causal snapshot isolation (CSI), CSI with commutative updates, and CSI with asynchronous updates. This model is useful in managing large-scale replicated data with different consistency guarantees to make suitable...
Most applications deployed in a Cloud require a high degree of availability. For the data layer, this means that data have to be replicated either within a data center or across Cloud data centers. While replication also allows to increase the performance of applications if data is read as the load can be distributed across replica sites, updates need special coordination among the sites and may have...
In this paper, we propose and implement a key-value store that supports MPI while allowing application access at any time without having to declaring in the same MPI communication world. This feature may significantly simplify the application design and allow programmers leverage the power of key-value store in an intuitive way. In our preliminary experiment results captured from a supercomputer at...
Cloud environments usually feature several geographically distributed data centers. In order to increase the scalability of applications, many Cloud providers partition data and distribute these partitions across data centers to balance the load. However, if the partitions are not carefully chosen, it might lead to distributed transactions. This is particularly expensive when applications require...
For datacenter applications that require tight synchronization, transactions are commonly employed for achieving concurrency while preserving correctness. Unfortunately, distributed transactions are hard to scale due to the decentralized lock acquisition and coordination protocols they employ. We investigate the use of a centralized lock broker architecture to improve the efficiency/scalability for...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.