Global supply chains of high value low volume products

Imperial Users onl

Impliance: A Next Generation Information Management Appliance

ably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch, based upon today's requirements and hardware capabilities, would it look anything like today's database systems?" In this paper, we introduce Impliance, a next-generation information management system consisting of hardware and software components integrated to form an easy-to-administer appliance that can store, retrieve, and analyze all types of structured, semi-structured, and unstructured information. We first summarize the trends that will shape information management for the foreseeable future. Those trends imply three major requirements for Impliance: (1) to be able to store, manage, and uniformly query all data, not just structured records; (2) to be able to scale out as the volume of this data grows; and (3) to be simple and robust in operation. We then describe four key ideas that are uniquely combined in Impliance to address these requirements, namely the ideas of: (a) integrating software and off-the-shelf hardware into a generic information appliance; (b) automatically discovering, organizing, and managing all data - unstructured as well as structured - in a uniform way; (c) achieving scale-out by exploiting simple, massive parallel processing, and (d) virtualizing compute and storage resources to unify, simplify, and streamline the management of Impliance. Impliance is an ambitious, long-term effort to define simpler, more robust, and more scalable information systems for tomorrow's enterprises.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

Low overhead concurrency control for partitioned main memory databases

Database partitioning is a technique for improving the performance of distributed OLTP databases, since "single partition" transactions that access data on one partition do not need coordination with other partitions. For workloads that are amenable to partitioning, some argue that transactions should be executed serially on each partition without any concurrency at all. This strategy makes sense for a main memory database where there are no disk or user stalls, since the CPU can be fully utilized and the overhead of traditional concurrency control, such as two-phase locking, can be avoided. Unfortunately, many OLTP applications have some transactions which access multiple partitions. This introduces network stalls in order to coordinate distributed transactions, which will limit the performance of a database that does not allow concurrency. In this paper, we compare two low overhead concurrency control schemes that allow partitions to work on other transactions during network stalls, yet have little cost in the common case when concurrency is not needed. The first is a light-weight locking scheme, and the second is an even lighter-weight type of speculative concurrency control that avoids the overhead of tracking reads and writes, but sometimes performs work that eventually must be undone. We quantify the range of workloads over which each technique is beneficial, showing that speculative concurrency control generally outperforms locking as long as there are few aborts or few distributed transactions that involve multiple rounds of communication. On a modified TPC-C benchmark, speculative concurrency control can improve throughput relative to the other schemes by up to a factor of two.National Science Foundation (U.S.). (Grant number IIS-0704424)National Science Foundation (U.S.). (Grant number IIS-0845643

An Analysis of Single-command Operations in a Mobile Rack (as/rs) Served by a Single Order Picker

A Mobile rack Automated Storage and Retrieval Systems (MAS/ RS) are picker-to-stock retrieval model which are a variation of the multi aisles AS/RS. This mobile storage system is composed of racks moving laterally on rails so that one can open an aisle between any two adjacent racks, the input/output system, the storage and retrieval (S/R) machine and the computer management system or the control system. Evaluating an AS/RS could be done using several performance indicators, the two most important ones are: The utilization rate of the S/R machine and the average time necessary to serve storage or retrieval requests (the travel time). The S/R machine could operate either in single command or in dual command. In a single command, the S/R machine executes either a storage or retrieval operation by cycle. The time necessary to execute a single command is said single cycle time. While in a dual command, the S/R machine executes a storage operation followed by a retrieval operation in the same cycle. The time needed to execute a dual command is said dual cycle time. In this paper our interest is concerned with the mathematical modeling of single-command operations in a Mobile rack (AS/RS) system. We developed a closed form analytical expression allowing an approximate calculation of the travel time of Mobile Racks-AS/RS. This expression was compared with an exact discrete expression developed earlier by one of the authors. The models developed in this work are used by Kouloughli et al to determine optimal dimensions of the mobile rack AS/RS that minimize expected travel times

A contribution to supply chain design under uncertainty

Dans le contexte actuel des chaînes logistiques, des processus d'affaires complexes et des partenaires étendus, plusieurs facteurs peuvent augmenter les chances de perturbations dans les chaînes logistiques, telles que les pertes de clients en raison de l'intensification de la concurrence, la pénurie de l'offre en raison de l'incertitude des approvisionnements, la gestion d'un grand nombre de partenaires, les défaillances et les pannes imprévisibles, etc. Prévoir et répondre aux changements qui touchent les chaînes logistiques exigent parfois de composer avec des incertitudes et des informations incomplètes. Chaque entité de la chaîne doit être choisie de façon efficace afin de réduire autant que possible les facteurs de perturbations. Configurer des chaînes logistiques efficientes peut garantir la continuité des activités de la chaîne en dépit de la présence d'événements perturbateurs. L'objectif principal de cette thèse est la conception de chaînes logistiques qui résistent aux perturbations par le biais de modèles de sélection d'acteurs fiables. Les modèles proposés permettent de réduire la vulnérabilité aux perturbations qui peuvent aV, oir un impact sur la continuité des opérations des entités de la chaîne, soient les fournisseurs, les sites de production et les sites de distribution. Le manuscrit de cette thèse s'articule autour de trois principaux chapitres: 1 - Construction d'un modèle multi-objectifs de sélection d'acteurs fiables pour la conception de chaînes logistiques en mesure de résister aux perturbations. 2 - Examen des différents concepts et des types de risques liés aux chaînes logistiques ainsi qu'une présentation d'une approche pour quantifier le risque. 3 - Développement d'un modèle d'optimisation de la fiabilité afin de réduire la vulnérabilité aux perturbations des chaînes logistiques sous l'incertitude de la sollicitation et de l'offre

Augmenting data warehousing architectures with hadoop

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementAs the volume of available data increases exponentially, traditional data warehouses struggle to transform this data into actionable knowledge. Data strategies that include the creation and maintenance of data warehouses have a lot to gain by incorporating technologies from the Big Data’s spectrum. Hadoop, as a transformation tool, can add a theoretical infinite dimension of data processing, feeding transformed information into traditional data warehouses that ultimately will retain their value as central components in organizations’ decision support systems. This study explores the potentialities of Hadoop as a data transformation tool in the setting of a traditional data warehouse environment. Hadoop’s execution model, which is oriented for distributed parallel processing, offers great capabilities when the amounts of data to be processed require the infrastructure to expand. Horizontal scalability, which is a key aspect in a Hadoop cluster, will allow for proportional growth in processing power as the volume of data increases. Through the use of a Hive on Tez, in a Hadoop cluster, this study transforms television viewing events, extracted from Ericsson’s Mediaroom Internet Protocol Television infrastructure, into pertinent audience metrics, like Rating, Reach and Share. These measurements are then made available in a traditional data warehouse, supported by a traditional Relational Database Management System, where they are presented through a set of reports. The main contribution of this research is a proposed augmented data warehouse architecture where the traditional ETL layer is replaced by a Hadoop cluster, running Hive on Tez, with the purpose of performing the heaviest transformations that convert raw data into actionable information. Through a typification of the SQL statements, responsible for the data transformation processes, we were able to understand that Hadoop, and its distributed processing model, delivers outstanding performance results associated with the analytical layer, namely in the aggregation of large data sets. Ultimately, we demonstrate, empirically, the performance gains that can be extracted from Hadoop, in comparison to an RDBMS, regarding speed, storage usage and scalability potential, and suggest how this can be used to evolve data warehouses into the age of Big Data