Technique for anchoring fasteners to honeycomb panels

Two-piece fastener bushing provides mounting surface for components on a three-inch thick honeycomb structure. Specially constructed starter drill and sheet metal drill permit drilling without misalignment. Tapered knife-edge cutting tool removes honeycomb core material without tearing the adjacent material

Reliability Testing of the PABS (Pedestrian and Bicycling Survey) Method

The Pedestrian and Bicycling Survey (PABS) is a questionnaire designed to be economical and straightforward to administer so that it can be used by local governments interested in measuring the amount and purposes of walking and cycling in their communities. In addition, it captures key sociodemographic characteristics of those participating in these activities. Methods: In 2009 and 2010 results from the 4-page mail-out/mail-back PABS were tested for reliability across 2 administrations (test-retest reliability). Two versions--early and refined--were tested separately with 2 independent groups of university students from 4 universities (N = 100 in group 1; N = 87 in group 2). Administrations were 7 to 9 days apart. Results: Almost all survey questions achieved adequate to excellent reliability. Conclusions: Transportation surveys have not typically been tested for reliability making the PABS questionnaire an important new option for improving information collection about travel behavior, particularly walking and cycling

Scalable data management in distributed information systems

[EN] In the era of cloud computing and huge information systems, distributed applications should manage dynamic workloads; i.e., the amount of client requests per time unit may vary frequently and servers should rapidly adapt their computing efforts to those workloads. Cloud systems provide a solid basis for this kind of applications but most of the traditional relational database systems are unprepared to scale up with this kind of distributed systems. This paper surveys different techniques being used in modern SQL, NoSQL and NewSQL systems in order to increase the scalability and adaptability in the management of persistent data. © 2011 Springer-Verlag.This work has been supported by EU FEDER and Spanish MICINN under research grants TIN2009-14460-C03-01 and TIN2010-17193Pallardó Lozoya, MR.; Esparza Peidro, J.; García Escriva, JR.; Decker, H.; Muñoz Escoí, FD. (2011). Scalable data management in distributed information systems. Lecture Notes in Computer Science. 7046:208-217. https://doi.org/10.1007/978-3-642-25126-9_31S2082177046Helland, P.: Life beyond distributed transactions: an apostate’s opinion. In: 3rd Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA, pp. 132–141 (2007)Finkelstein, S., Jacobs, D., Brendle, R.: Principles for inconsistency. In: 4th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA (2009)Chang, F., Dean, J., Ghemawat, S., Hsieh, W.C., Wallach, D.A., Burrows, M., Chandra, T., Fikes, A., Gruber, R.: Bigtable: A distributed storage system for structured data. In: 7th Symp. on Operat. Syst. Design and Implem. (OSDI), pp. 205–218. USENIX Assoc., Seattle (2006)Cooper, B.F., Baldeschwieler, E., Fonseca, R., Kistler, J.J., Narayan, P.P.S., Neerdaels, C., Negrin, T., Ramakrishnan, R., Silberstein, A., Srivastava, U., Stata, R.: Building a cloud for Yahoo! IEEE Data Eng. Bull. 32, 36–43 (2009)DeCandia, G., Hastorun, D., Jampani, M., Kakulapati, G., Lakshman, A., Pilchin, A., Sivasubramanian, S., Vosshall, P., Vogels, W.: Dynamo: Amazon’s highly available key-value store. In: 21st ACM Symp. on Operat. Syst. Princ. (SOSP), Stevenson, Washington, USA, pp. 205–220 (2007)Stonebraker, M., Madden, S., Abadi, D.J., Harizopoulos, S., Hachem, N., Helland, P.: The end of an architectural era (it’s time for a complete rewrite). In: 33rd Intnl. Conf. on Very Large Data Bases (VLDB), pp. 1150–1160. ACM Press, Vienna (2007)Lomet, D.B., Fekete, A., Weikum, G., Zwilling, M.J.: Unbundling transaction services in the cloud. In: 4th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA (2009)Campbell, D.G., Kakivaya, G., Ellis, N.: Extreme scale with full SQL language support in Microsoft SQL Azure. In: Intnl. Conf. on Mngmnt. of Data (SIGMOD), pp. 1021–1024. ACM, New York (2010)Levandoski, J.J., Lomet, D., Mokbel, M.F., Zhao, K.K.: Deuteronomy: Transaction support for cloud data. In: 5th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA, pp. 123–133 (2011)Helland, P., Campbell, D.: Building on quicksand. In: 4th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA (2009)Muñoz-Escoí, F.D., García-Escrivá, J.R., Pallardó-Lozoya, M.R., Esparza-Peidro, J.: Managing scalable persistent data. Technical Report ITI-SIDI-2011/003, Instituto Tecnológico de Informática, Universitat Politècnica de València, Spain (2011)Agrawal, D., El Abbadi, A., Antony, S., Das, S.: Data management challenges in cloud computing infrastructures. In: 6th Intnl. Wshop. on Databases in Networked Information Systems (DNIS), Aizu-Wakamatsu, Japan, pp. 1–10 (2010)Stonebraker, M.: The case for shared nothing. IEEE Database Eng. Bull. 9, 4–9 (1986)Alonso, G., Kossmann, D., Roscoe, T.: SwissBox: An architecture for data processing appliances. In: 5th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA, pp. 32–37 (2011)Baker, J., Bond, C., Corbett, J.C., Furman, J.J., Khorlin, A., Larson, J., Léon, J.M., Li, Y., Lloyd, A., Yushprakh, V.: Megastore: Providing scalable, highly available storage for interactive services. In: 5th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA, pp. 223–234 (2011)Curino, C., Jones, E.P.C., Popa, R.A., Malviya, N., Wu, E., Madden, S., Balakrishnan, H., Zeldovich, N.: Relational cloud: A database-as-a-service for the cloud. In: 5th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA, pp. 235–240 (2011)Das, S., Agrawal, D., El Abbadi, A.: ElasTraS: An elastic transactional data store in the cloud. CoRR abs/1008.3751 (2010)Vogels, W.: Eventually consistent. Commun. ACM 52, 40–44 (2009)Breitbart, Y., Korth, H.F.: Replication and consistency: being lazy helps sometimes. In: 16th ACM Symp. on Princ. of Database Syst., PODS 1997, pp. 173–184. ACM, New York (1997)Brantner, M., Florescu, D., Graf, D.A., Kossmann, D., Kraska, T.: Building a database on S3. In: Intnl. Conf. on Mngmnt. of Data (SIGMOD), pp. 251–264. ACM Press, Vancouver (2008)Lakshman, A., Malik, P.: Cassandra: a decentralized structured storage system. Operating Systems Review 44, 35–40 (2010)Burrows, M.: The Chubby lock service for loosely-coupled distributed systems. In: 7th Symp. on Operat. Syst. Design and Implem. (OSDI), pp. 335–350. USENIX Assoc., Seattle (2006)Junqueira, F.P., Reed, B.: The life and times of a ZooKeeper. In: 28th Annual ACM Symp. on Princ. of Distrib. Comp. (PODC), p. 4. ACM Press, Calgary (2009)MacCormick, J., Murphy, N., Najork, M., Thekkath, C.A., Zhou, L.: Boxwood: Abstractions as the foundation for storage infrastructure. In: 6th Simp. on Operat. Syst. Design and Impl. (OSDI), pp. 105–120. USENIX Assoc., San Francisco (2004)Stonebraker, M., Cattell, R.: Ten rules for scalable performance in ”simple operation” datastores. Commun. ACM 54, 72–80 (2011)Amazon Web Services LLC: Amazon SimpleDB (2011), http://aws.amazon.com/simpledb/Lamport, L.: The part-time parliament. ACM Trans. Comput. Syst. 16, 133–169 (1998)Bernstein, P.A., Reid, C.W., Das, S.: Hyder - a transactional record manager for shared flash. In: 5th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA, pp. 9–20 (2011)Bonnet, P., Bouganim, L.: Flash device support for database management. In: 5th Biennial Conf. on Innov. Data Syst. Research (CIDR), Asilomar, CA, USA, pp. 1–8 (2011)Microsoft Corp.: Windows Azure: Microsoft’s cloud services platform (2011), http://www.microsoft.com/windowsazure/VoltDB, Inc.: VoltDB technical overview: Next generation open-source SQL database with ACID for fast-scaling OLTP applications (2010), Downloadable from: http://voltdb.com/_pdf/VoltDBTechnicalOverviewWhitePaper.pd

Functional pearl: a SQL to C compiler in 500 lines of code

We present the design and implementation of a SQL query processor that outperforms existing database systems and is written in just about 500 lines of Scala code - a convincing case study that high-level functional programming can handily beat C for systems-level programming where the last drop of performance matters. The key enabler is a shift in perspective towards generative programming. The core of the query engine is an interpreter for relational algebra operations, written in Scala. Using the open-source LMS Framework (Lightweight Modular Staging), we turn this interpreter into a query compiler with very low effort. To do so, we capitalize on an old and widely known result from partial evaluation known as Futamura projections, which state that a program that can specialize an interpreter to any given input program is equivalent to a compiler. In this pearl, we discuss LMS programming patterns such as mixed-stage data structures (e.g. data records with static schema and dynamic field components) and techniques to generate low-level C code, including specialized data structures and data loading primitives

Class of Service in the High Performance Storage System

Quality of service capabilities are commonly deployed in archival mass storage systems as one or more client-specified parameters to influence physical location of data in multi-level device hierarchies for performance or cost reasons. The capabilities of new high-performance storage architectures and the needs of data-intensive applications require better quality of service models for modern storage systems. HPSS, a new distributed, high-performance, scalable, storage system, uses a Class of Service (COS) structure to influence system behavior. The authors summarize the design objectives and functionality of HPSS and describes how COS defines a set of performance, media, and residency attributes assigned to storage objects managed by HPSS servers. COS definitions are used to provide appropriate behavior and service levels as requested (or demanded) by storage system clients. They compare the HPSS COS approach with other quality of service concepts and discuss alignment possibilities

A grid-based infrastructure for distributed retrieval

In large-scale distributed retrieval, challenges of latency, heterogeneity, and dynamicity emphasise the importance of infrastructural support in reducing the development costs of state-of-the-art solutions. We present a service-based infrastructure for distributed retrieval which blends middleware facilities and a design framework to ‘lift’ the resource sharing approach and the computational services of a European Grid platform into the domain of e-Science applications. In this paper, we give an overview of the DILIGENT Search Framework and illustrate its exploitation in the field of Earth Science

Speedy Transactions in Multicore In-Memory Databases

Silo is a new in-memory database that achieves excellent performance and scalability on modern multicore machines. Silo was designed from the ground up to use system memory and caches efficiently. For instance, it avoids all centralized contention points, including that of centralized transaction ID assignment. Silo's key contribution is a commit protocol based on optimistic concurrency control that provides serializability while avoiding all shared-memory writes for records that were only read. Though this might seem to complicate the enforcement of a serial order, correct logging and recovery is provided by linking periodically-updated epochs with the commit protocol. Silo provides the same guarantees as any serializable database without unnecessary scalability bottlenecks or much additional latency. Silo achieves almost 700,000 transactions per second on a standard TPC-C workload mix on a 32-core machine, as well as near-linear scalability. Considered per core, this is several times higher than previously reported results.Engineering and Applied Science

Expressiveness of Temporal Query Languages: On the Modelling of Intervals, Interval Relationships and States

Storing and retrieving time-related information are important, or even critical, tasks on many areas of Computer Science (CS) and in particular for Artificial Intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: it Templog in the context of temporal deductive databases, and it TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts