DOCSLIB.ORG

Resume of Dr. Michael J. Bisconti

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Resume of Dr. Michael J. Bisconti Table of Contents This file contains, in order: Time Savers Experience Matrix Resume _________________________ 1 Time Savers There are a number of things we can do to save everyone’s time. In addition to resume information there are a number of common questions that employers and recruiters have. Here is an FAQ that addresses these questions. (We may expand this FAQ over time.) Frequently Asked Questions 1099 Multiple Interviewers Severance Pay Contract End Date Multiple Interviews Technical Exam Contract Job Need/Skill Assessment Interview Temporary Vs. Permanent Contract Rate Payment Due Dates U.S. Citizenship Drug Testing Permanent Job W2 Face-to-face Interview Phone Interview Word Resume Job Hunt Progress Salary Are you a U.S. citizen? Yes. Do you have a Word resume? Yes, and I also have an Adobe PDF resume. Do you prefer temporary (contract) or permanent employment? Neither, since, in the end, they are equivalent. Will you take a drug test? 13 drug tests taken and passed. Do you work 1099? Yes, but I give W2 payers preference. Do you work W2? Yes, and I work 1099 as well but I give W2 payers preference. How is your job search going? See 1.2 Job Hunt Progress. What contract rate do you expect? $65 to $85/hr. W2 and see the 2.5 Quick Rates Guide. What salary do you expect? 120k to 130k/yr. and see the 2.5 Quick Rates Guide. When do you expect to be paid? Weekly or biweekly and weekly payers will be given preference. Will you do a face-to-face interview? Yes, but I prefer a Skype or equivalent interview because gas is so expensive and time is so valuable. Is a phone interview required? Yes. Will you do multiple interviews? Yes, but no more than 2. Will you do technical assessment interviews, which seek to determine if your skills will meet the client’s needs? Yes. Will you interview with multiple people at a given company? Yes, but no more than 2. Will you take a contract job? Yes. Will you take a permanent job? Yes, but I look at contract jobs first since the hiring process is faster. Will you take a technical exam? 85 tests taken and passed. Do you require a contract end date in the contract? Yes. Do you require severance pay? Yes. If the contract is terminated by you/your client before the contract end date, you, the paying agency, will pay me an amount equal to one week of salary in addition to my hourly wages and included in my last paycheck. 2 Experience Matrix Subject Activity Time Spent .Net Framework Installation, Dumb Utilization Considerable (≥ 20 years) 4th Dimension Query Development Adequate (≥ 2 years) Accounting Accounts Receivable, Accounts Payable Sufficient (≥ 5 years) Active Directory Dumb Utilization Considerable (≥ 20 years) Microsoft Access Generated Active Active Server Pages Sufficient (≥ 5 years) Server Pages ActiveX Installation, Utilization, Dumb Utilization Considerable (≥ 20 years) ADABAS Query Development Adequate (≥ 2 years) Adabas D Query Development Adequate (≥ 2 years) Ajax Dumb Utilization Considerable (≥ 20 years) Alerts Creation, Utilization Significant (≥ 10 years) Alpha Five Query Development Adequate (≥ 2 years) Altibase Query Development Adequate (≥ 2 years) Database Creation, Database Amazon Web Services Significant (≥ 10 years) Configuration Analysis Services (OLAP Services) Cube Creation Sufficient (≥ 5 years) Apache Cassandra Query Development Adequate (≥ 2 years) Apache Derby Query Development Adequate (≥ 2 years) Architecture (2Tier, 2½Tier, 3Tier & Understanding Sufficient (≥ 5 years) Other NTier) AS/SET Utilization Marginal (≥ 1 year) Microsoft Access Generated Active ASP Sufficient (≥ 5 years) Server Pages Aster Data Query Development Adequate (≥ 2 years) Attributes Table Development Considerable (≥ 20 years) Auditing SQL Server Auditing Configuration Sufficient (≥ 5 years) Database Creation, Database AWS Sufficient (≥ 5 years) Configuration Bachman Query Development Adequate (≥ 2 years) Backends All Tasks Considerable (≥ 20 years) Manual, Scheduled, Disk, Tape, CD, Backups DVD, External Hard Drive, Internet Considerable (≥ 20 years) Storage, SAN Banking Industry SQL Server DBA Sufficient (≥ 5 years) Banyan Utilization Marginal (≥ 1 year) Batch Processing SQL Queries, DTS, SSIS Significant (≥ 10 years) BCP/Export/Import Queries, Ad Hoc Work In Query Analyzer Significant (≥ 10 years) Counter Selection & Programming, Benchmarks Considerable (≥ 20 years) Metrics Records & Graphs Bit Utilization Marginal (≥ 1 year) BlackRay Query Development Adequate (≥ 2 years) Blocking Analysis, Activity Monitor, Kill Execution Significant (≥ 10 years) BMC Utilization Marginal (≥ 1 year) 3 Experience Matrix Subject Activity Time Spent Borland C++ Programming Adequate (≥ 2 years) Business Analysis Development, Understanding Considerable (≥ 20 years) Business Intelligence DTS/SSIS 25%, SSAS 5%, SSRS 70% Considerable (≥ 20 years) Business Objects Utilization Adequate (≥ 2 years) C Programming Significant (≥ 10 years) C# Programming Adequate (≥ 2 years) C++ Programming Considerable (≥ 20 years) Cache Hit Ratio Counters, Metrics, Graphs Considerable (≥ 20 years) CA-Datacom Query Development Adequate (≥ 2 years) Capacity Planning Counters, Metrics, Graphs Considerable (≥ 20 years) Case Tools ERWin, Visio Considerable (≥ 20 years) CC:Mail Utilization Marginal (≥ 1 year) CGI Dumb Utilization Considerable (≥ 20 years) Change Data Capture - 2012 Automation Logical Design Adequate (≥ 2 years) Change Data Capture - Traditional Logical Design Sufficient (≥ 5 years) Checkdb Database Maintenance Considerable (≥ 20 years) CICS Utilization Marginal (≥ 1 year) Clarion Utilization Marginal (≥ 1 year) Classes Web Development Considerable (≥ 20 years) Clients Utilization Considerable (≥ 20 years) Clipper Utilization Marginal (≥ 1 year) Clist Utilization Marginal (≥ 1 year) Database Creation, Database Cloud Sufficient (≥ 5 years) Configuration Clustering Installation, Administration, Instructor Significant (≥ 10 years) Clustrix Utilization Marginal (≥ 1 year) CMS Utilization Marginal (≥ 1 year) COBOL Programming Adequate (≥ 2 years) Cognos Utilization Marginal (≥ 1 year) Columns (Fields) Table Development Considerable (≥ 20 years) Command Line Multiple Skills Considerable (≥ 20 years) Communications Configuration Adequate (≥ 2 years) Compilation Programming Significant (≥ 10 years) Compliance Sarbanes-Oxley, HIPPA Sufficient (≥ 5 years) Configuration Options Database Development Considerable (≥ 20 years) Server Database Connection String Connectivity Considerable (≥ 20 years) Programming Constraints Database Development Significant (≥ 10 years) Constraints Database Development Significant (≥ 10 years) CRM Utilization Marginal (≥ 1 year) Crystal Reports All Tasks Considerable (≥ 20 years) CSQL Query Development Adequate (≥ 2 years) CUBRID Utilization Marginal (≥ 1 year) Customer Relationship Management SQL Server Backend Significant (≥ 10 years) D & B Utilization Marginal (≥ 1 year) Daffodil database Query Development Adequate (≥ 2 years) 4 Experience Matrix Subject Activity Time Spent Daily Maintenance All Tasks Considerable (≥ 20 years) Data Dictionaries Creation, Utilization Significant (≥ 10 years) Data Flow Diagrams Creation, Utilization Sufficient (≥ 5 years) Data Warehouses Creation, Utilization Sufficient (≥ 5 years) Database Administration All Tasks Considerable (≥ 20 years) Database Administrator All Tasks Considerable (≥ 20 years) Database Architect Logical Design, Physical Design Considerable (≥ 20 years) Database Architecture Logical Design, Physical Design Considerable (≥ 20 years) Creation, Configuration, Scheduling, Database Maintenance Plans Considerable (≥ 20 years) Utilization Database Management Library Query Development Adequate (≥ 2 years) Database Mirroring Programming Sufficient (≥ 5 years) Databases All Tasks Considerable (≥ 20 years) Datacenters Direct Server Access Significant (≥ 10 years) DataEase Query Development Adequate (≥ 2 years) Dataphor Query Development Adequate (≥ 2 years) Datatype Conversion SQL Queries Significant (≥ 10 years) DB Query Development Adequate (≥ 2 years) DB2 Query Development Adequate (≥ 2 years) DB2/2 Query Development Adequate (≥ 2 years) DBA All Tasks Considerable (≥ 20 years) Query Development, Dasbase dBase Considerable (≥ 20 years) Configuration Database Checking, Table Checking, DBCC (Database Consistency Checking) Automation Scripts, Nightly Batch Considerable (≥ 20 years) Processing DCL (Data Control Language) Security Significant (≥ 10 years) DDL (Data Definition Language) Logical Modelling, Queries Considerable (≥ 20 years) Deadlocks Resolution Significant (≥ 10 years) Debugging Programming Considerable (≥ 20 years) Defragmentation Database Maintenance Significant (≥ 10 years) Delphi Query Development Adequate (≥ 2 years) Deployment Sourcesafe, Team Foundation Server Significant (≥ 10 years) Derby aka Java DB Query Development Adequate (≥ 2 years) Designer 2000 Utilization Marginal (≥ 1 year) Developer All Tasks Considerable (≥ 20 years) Developer 2000 Utilization Marginal (≥ 1 year) Development All Tasks Considerable (≥ 20 years) Devices Creation Considerable (≥ 20 years) DFU Utilization Marginal (≥ 1 year) DG Dictionary Utilization Marginal (≥ 1 year) Diagnostics SQL Server, sqldiag.exe Considerable (≥ 20 years) Dialog Manager Utilization Marginal (≥ 1 year) Disaster Recovery Planning & Testing Significant (≥ 10 years) DML (Data Maniplation Language) Queries Considerable (≥ 20 years) Documentation Author Considerable (≥ 20 years) 5 Experience Matrix Subject Activity Time Spent Documentation – Policies And Author Considerable
Load more

Recommended publications
  • LIST of NOSQL DATABASES [Currently 150]
    Your Ultimate Guide to the Non - Relational Universe! [the best selected nosql link Archive in the web] ...never miss a conceptual article again... News Feed covering all changes here! NoSQL DEFINITION: Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open-source and horizontally scalable. The original intention has been modern web-scale databases. The movement began early 2009 and is growing rapidly. Often more characteristics apply such as: schema-free, easy replication support, simple API, eventually consistent / BASE (not ACID), a huge amount of data and more. So the misleading term "nosql" (the community now translates it mostly with "not only sql") should be seen as an alias to something like the definition above. [based on 7 sources, 14 constructive feedback emails (thanks!) and 1 disliking comment . Agree / Disagree? Tell me so! By the way: this is a strong definition and it is out there here since 2009!] LIST OF NOSQL DATABASES [currently 150] Core NoSQL Systems: [Mostly originated out of a Web 2.0 need] Wide Column Store / Column Families Hadoop / HBase API: Java / any writer, Protocol: any write call, Query Method: MapReduce Java / any exec, Replication: HDFS Replication, Written in: Java, Concurrency: ?, Misc: Links: 3 Books [1, 2, 3] Cassandra massively scalable, partitioned row store, masterless architecture, linear scale performance, no single points of failure, read/write support across multiple data centers & cloud availability zones. API / Query Method: CQL and Thrift, replication: peer-to-peer, written in: Java, Concurrency: tunable consistency, Misc: built-in data compression, MapReduce support, primary/secondary indexes, security features.
    [Show full text]
  • Ordered Indexing Methods for Data Streaming Applications
    IT 11 061 Examensarbete 45 hp Augusti 2011 Ordered indexing methods for data streaming applications Sobhan Badiozamany Institutionen för informationsteknologi Department of Information Technology Abstract Ordered indexing methods for data streaming applications Sobhan Badiozamany Teknisk- naturvetenskaplig fakultet UTH-enheten Many data streaming applications need ordered indexing techniques in order to maintain statistics required to answer historical and continuous queries. Conventional Besöksadress: DBMS indexing techniques are not specifically designed for data streaming Ångströmlaboratoriet Lägerhyddsvägen 1 applications; this motivates an investigation on comparing the scalability of different Hus 4, Plan 0 ordered indexing techniques in data streaming applications. This master thesis compares two ordered indexing techniques – tries and B-trees - with each other in Postadress: the context of data streaming applications. Indexing data streams also requires Box 536 751 21 Uppsala supporting high insertion and deletion rates. Two deletion strategies, bulk deletion and incremental deletion were also compared in this project. The Linear Road Telefon: Benchmark, SCSQ-LR and Amos II were comprehensively used to perform the 018 – 471 30 03 scalability comparisons and draw conclusions. Telefax: 018 – 471 30 00 Hemsida: http://www.teknat.uu.se/student Handledare: Tore Risch Ämnesgranskare: Tore Risch Examinator: Anders Jansson IT 11 061 Sponsor: This project is supported by VINNOVA, grant 2007-02916. Tryckt av: Reprocentralen ITC Acknowledgments The idea of this project was suggested by Prof. Tore Risch. Without his continuous valuable supervision, support, guidance and encouragement it was impossible to perform this study. I would also like to thank Erik Zeitler and Thanh Truong. Erik helped me in understanding the LRB and also running the experiments.
    [Show full text]
  • Data Management in Set-Top Box Electronic Programming Guides
    Data Management in Set-Top Box Electronic Programming Guides Abstract: The electronic programming guide (EPG) offers the most fully developed interactive browsing service on digital television, enabling users to search, filter and customize program listings and even control access to some content. These capabilities add significant data management considerations to the design of new set-top boxes. Seeking a proven solution, a handful of vendors have begun incorporating off-the-shelf database technology into their set-top boxes. This paper explores EPG data management, with the goal of educating developers and improving their software results. It maps emerging digital TV standards and the set-top box technology environment, explores data management requirements, and presents typical data objects and interrelationships found in programming guides. In examining solutions, the paper focuses on one relatively new type of data management, the in-memory database system (IMDS), which improves data management performance and minimizes resource demands through a streamlined design. Code examples and sample database schema focus on efficiencies gained by implementing set-top box data management using an off-the-shelf database system. McObject LLC 22525 SE 64th Place Suite 302 Issaquah, WA 98027 Phone: 425-831-5964 E-mail: [email protected] www.mcobject.com Copyright 2002, McObject LLC Introduction. Digital television has no doubt emerged as one of the most fully realized networked multimedia technologies. While other convergence applications strive to move into the mainstream, digital TV is already delivering hundreds of channels, a crisp clear picture, and better-than-CD-quality audio to millions of users. The period of transition from analog to digital broadcasting could last a decade or more, but eventually all broadcasters will move to the digital domain.
    [Show full text]
  • An Overview of the Usage of Default Passwords (Extended Version)
    An Overview of the Usage of Default Passwords (extended version) Brandon Knieriem, Xiaolu Zhang, Philip Levine, Frank Breitinger, and Ibrahim Baggili Cyber Forensics Research and Education Group (UNHcFREG) Tagliatela College of Engineering University of New Haven, West Haven CT, 06516, United States fbknie1, [email protected],fXZhang, FBreitinger, [email protected] Summary. The recent Mirai botnet attack demonstrated the danger of using default passwords and showed it is still a major problem in 2017. In this study we investigated several common applications and their pass- word policies. Specifically, we analyzed if these applications: (1) have default passwords or (2) allow the user to set a weak password (i.e., they do not properly enforce a password policy). In order to understand the developer decision to implement default passwords, we raised this question on many online platforms or contacted professionals. Default passwords are still a significant problem. 61% of applications inspected initially used a default or blank password. When changing the password, 58% allowed a blank password, 35% allowed a weak password of 1 char- acter. Key words: Default passwords, applications, usage, security 1 Introduction Security is often disregarded or perceived as optional to the average consumer which can be a drawback. For instance, in October 2016 a large section of the In- ternet came under attack. This attack was perpetuated by approximately 100,000 Internet of Things (IoT) appliances, refrigerators, and microwaves which were compromised and formed the Mirai botnet. Targets of this attack included Twit- ter, reddit and The New York Times all of which shut down for hours.
    [Show full text]
  • Extremedb™ Kernel Mode Edition
    Product Datasheet eXtreme DB Kernel Mode Edit ion “The Linux kernel is no place for 'self-expressive fancy.' ” Kernel -mode main memory storage solution for -- Andrew Morton, performance-critical applications Open Source Development Labs Overview eXtreme DB-KM achieves this by providing direct The essential part of any operating system is the operating access to kernel data . The eXtreme DB-KM run-time system “kernel”. The kernel is responsible for resource maps its databases directly into the driver or kernel allocation, scheduling, low-level hardware interfaces, module address space, providing direct pointers to the networking, security etc. data and eliminating expensive buffer management. The Challenge The eXtreme DB run-time code is directly linked with the module, so remote procedure calls are eliminated Increasingly, system software vendors try to put more from the execution path. As a consequence, the application functionality into the operating system kernel. execution path generally requires just a few CPU Running parts of the application in the kernel adds to the entire instructions. Concurrent access is coordinated by the system’s performance tremendously. At the same time, many database run-time. The databases are also made modern application architectures require sophisticated data available to user-mode applications via a set of public management functions such as concurrent data access, interfaces implemented via system calls. transaction control and complex searches. Putting the data management portion of the system into the kernel makes those functions faster and more robust. For example, firewalls and other types of computer security User Program software (access control applications, etc) must run their policy engines as kernel components, to achieve needed performance.
    [Show full text]
  • IMDS-NVDIMM-Paper
    Database Persistence, Without The Performance Penalty Benchmarking McObject’s In-Memory Database System With AgigA Tech’s Non-Volatile DIMM Technology McObject LLC 22525 SE 64th Place Suite 302 Issaquah, WA 98027 Phone: 425-888-8505 E-mail: [email protected] www.mcobject.com Copyright 2014, McObject LLC In the past decade, in-memory database systems (IMDSs) have emerged as the database solution for many real-time applications. An IMDS stores data entirely in main memory (DRAM), leveraging memory’s superior read/write speed while eliminating the file I/O, cache management, data transfer and other overhead inherent in traditional database management systems (DBMSs) that are designed to store records on persistent media. As a result, in- memory database systems perform an order of magnitude faster than their disk-based counterparts. A disadvantage to DRAM-based storage is volatility. What happens to records stored in main memory if power disappears? Without some kind of backup or log, they’re gone – and some applications require a higher level of data durability and recoverability. As a solution, IMDS vendors offer transaction logging, which keeps a record of changes to the database. But this logging requires writing to persistent storage. Will re-introducing these “database writes” eliminate IMDSs’ performance advantage over disk-based DBMSs? Another solution is to deploy an IMDS using DRAM that is backed up by battery power. However, disadvantages of this “battery-backed RAM” or non-volatile RAM (NVRAM) include batteries’ restrictive temperature requirements and the risk of leaking corrosive and toxic fluids. Battery-backed RAM also suffers from limited length of storage (data typically must be transferred from the chip within 72 hours of failover to battery), long re-charge cycles, finite battery shelf life, and overall high cost-of-ownership.
    [Show full text]
  • Virtual Denormalization Via Array Index Reference for Main Memory OLAP
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 27, NO. X, XXXXX 2015 1 Virtual Denormalization via Array Index Reference for Main Memory OLAP Yansong Zhang, Xuan Zhou, Ying Zhang, Yu Zhang, Mingchuan Su, and Shan Wang Abstract—Denormalization is a common tactic for enhancing performance of data warehouses, though its side-effect is quite obvious. Besides being confronted with update abnormality, denormalization has to consume additional storage space. As a result, this tactic is rarely used in main memory databases, which regards storage space, i.e., RAM, as scarce resource. Nevertheless, our research reveals that main memory database can benefit enormously from denormalization, as it is able to remarkably simplify the query processing plans and reduce the computation cost. In this paper, we present A-Store, a main memory OLAP engine customized for star/snowflake schemas. Instead of generating fully materialized denormalization, A-Store resorts to virtual denormalization by treating array indexes as primary keys. This design allows us to harvest the benefit of denormalization without sacrificing additional RAM space. A-Store uses a generic query processing model for all SPJGA queries. It applies a number of state-of-the-art optimization methods, such as vectorized scan and aggregation, to achieve superior performance. Our experiments show that A-Store outperforms the most prestigious MMDB systems significantly in star/snowflake schema based query processing. Index Terms—Main-memory, OLAP, denormalization, A-Store, array index. Ç 1INTRODUCTION HE purpose of database normalization is to eliminate strategy of denormalization. Recent development of MMDB Tdata redundancy, so as to save storage space and avoid [1], [2], [3] has shown that simplicity of data processing update abnormality.
    [Show full text]
  • In-Memory Database Systems (Imdss) Beyond the Terabyte Size Boundary
    In-Memory Database Systems (IMDSs) Beyond the Terabyte Size Boundary A BENCHMARK REPORT Abstract: In-memory database systems (IMDSs) hold out the promise of breakthrough performance for time-sensitive, data-intensive tasks. Yet IMDSs’ compatibility with very large databases (VLDBs) has been largely uncharted. This benchmark analysis fills the information gap and pushes the boundaries of IMDS size and performance. Using McObject’s 64-bit eXtreme DB- 64, the application creates a 1.17 Terabyte, 15.54 billion row database on a 160-core Linux- based SGI® Altix® 4700 server. It measures time required for database provisioning, backup and restore. In SELECT, JOIN and SUBQUERY tests, benchmark results range as high as 87.78 million query transactions per second. The report also examines efficiency in utilizing all of the test bed system’s 160 processors, and includes full database schema and relevant application source code. McObject LLC 22525 SE 64th Place Suite 302 Issaquah, WA 98027 Phone: 425-831-5964 E-mail: [email protected] www.mcobject.com Copyright 2007, McObject LLC Introduction The Louisiana Immersive Technologies Enterprise (LITE), hosted in the Research Park of the University of Louisiana at Lafayette, is a facility designed for exploring technological boundaries. LITE recently added a massive computing component to its already world-leading infrastructure: a 160-processor SGI® Altix® 4700 server with 4.1 terabytes of memory. The acquisition positions LITE in the top echelon of computing resources available for private and public sector projects. The facility targets the most demanding analytical tasks: seismic modeling for energy exploration, signals and imagery intelligence for the military, business intelligence, real-time automobile impact simulations, geospatial analysis, and many other projects that require crunching vast amounts of data in limited time.
    [Show full text]
  • Exploring Code Size and Footprint, and In-Memory Database Techniques to Minimize Footprint
    Exploring Code Size and Footprint, and In-Memory Database Techniques to Minimize Footprint Are Either Relevant to Embedded Systems in the IoT Age? Abstract: The terms ‘code size’ and ‘footprint’ are often used interchangeably. But, they are not the same; code size is a subset of footprint. This paper will explain the differentiation and relevance, then proceed to describe some of the techniques employed within eXtremeDB® to minimize footprint. McObject LLC 33309 1st Way S. A208 Federal Way, WA 98003 Phone: 425-888-8505 E-mail: [email protected] www.mcobject.com Copyright 2020, McObject LLC 1. Are ‘footprint’ and ‘code size’ the same thing? 2. And, is either relevant in the current age? 1. No. Code size is a subset of footprint. In the context of an in-memory database system, ‘footprint’ consists of: Code size Stack memory used DRAM used for anything other than raw data With respect to code size, we must have an all-encompassing view. We’re not the only embedded database vendor that boasts about a ‘small footprint’. But, eXtremeDB might be the only embedded database that has no external dependencies. For purposes of this discussion, it means that using the eXtremeDB core in-memory database does not cause the application to link in the C run-time library for things like malloc/free, string operations, etc. A claim of ‘small footprint’ is hollow when the application is forced to link a megabyte of the C run-time library. 2. Yes, it’s relevant. Code size matters. We can impute two things from a small code size: The function call depth and the number of CPU instructions for any given operation.
    [Show full text]
  • Partner Directory Wind River Partner Program
    PARTNER DIRECTORY WIND RIVER PARTNER PROGRAM The Internet of Things (IoT), cloud computing, and Network Functions Virtualization are but some of the market forces at play today. These forces impact Wind River® customers in markets ranging from aerospace and defense to consumer, networking to automotive, and industrial to medical. The Wind River® edge-to-cloud portfolio of products is ideally suited to address the emerging needs of IoT, from the secure and managed intelligent devices at the edge to the gateway, into the critical network infrastructure, and up into the cloud. Wind River offers cross-architecture support. We are proud to partner with leading companies across various industries to help our mutual customers ease integration challenges; shorten development times; and provide greater functionality to their devices, systems, and networks for building IoT. With more than 200 members and still growing, Wind River has one of the embedded software industry’s largest ecosystems to complement its comprehensive portfolio. Please use this guide as a resource to identify companies that can help with your development across markets. For updates, browse our online Partner Directory. 2 | Partner Program Guide MARKET FOCUS For an alphabetical listing of all members of the *Clavister ..................................................37 Wind River Partner Program, please see the Cloudera ...................................................37 Partner Index on page 139. *Dell ..........................................................45 *EnterpriseWeb
    [Show full text]
  • A General Temporal Data Model and the Structured Population Event History Register
    A General Temporal Data Model and the Structured Population Event History Register Samuel J. Clark* Abstract Longitudinal projects collect, store and manipulate large amounts of data that describe the histories of individual people, households and other units of analysis. These are temporal data that often describe inter-related histories, and consequently the structure of such data is complex and managing them can be difficult. Several existing data models successfully address this challenge but with significantly different solutions, and as a result, data stored using these different data models are hard to compare or merge. Moreover when ongoing projects use different data models, it is difficult to design an investigation that utilizes or collects data from more than one project because their individual data models are largely incompatible. Multi-site longitudinal investigations including large scale vaccine and behavioral intervention trials are and will become more common in the future making it an urgent matter to develop a standard temporal framework to guide the storage and manipulation of complex temporal data describing the histories of people (and households and other aggregations of people) living in multiple populations. This work begins to address this challenge by presenting 1) an abstract temporal data model that can represent an arbitrary range of inter-related temporal trajectories – the General Temporal Data Model or GTDM, 2) a relational database implementation of the GTDM that is able to store an arbitrary range of inter-related temporal trajectories with a single static relational schema – the Structured Population Event History Register or SPEHR, and 3) a relational database schema based on SPEHR that can store the contents of many SPHER-based databases allowing data from different longitudinal projects to be easily merged and managed together.
    [Show full text]
  • Visual C# .NET Developer's Handbook
    Visual C# .NET Developer's Handbook John Paul Mueller Associate Publisher: Richard Mills Acquisitions and Developmental Editor: Denise Santoro Lincoln Editor: Colleen Wheeler Strand Production Editor: Kylie Johnston Technical Editor: Ross Russell Mullen Graphic Illustrator: Tony Jonick Electronic Publishing Specialist: Nila Nichols Proofreaders: Amey Garber, Dave Nash, Laurie O'Connell, Yariv Rabinovitch, Nancy Riddiough Indexer: Ron Strauss CD Coordinator: Dan Mummert CD Technician: Kevin Ly Cover Designer: Carol Gorska/Gorska Design Cover Photographer: Glen Allison/PhotoDisc Copyright © 2002 SYBEX Inc., 1151 Marina Village Parkway, Alameda, CA 94501. World rights reserved. The author created reusable code in this publication expressly for reuse by readers. Sybex grants readers limited permission to reuse the code found in this publication or its accompanying CD-ROM so long as the author is attributed in any application containing the reusable code and the code itself is never distributed, posted online by electronic transmission, sold, or commercially exploited as a stand-alone product. Aside from this specific exception concerning reusable code, no part of this publication may be stored in a retrieval system, transmitted, or reproduced in any way, including but not limited to photocopy, photograph, magnetic, or other record, without the prior agreement and written permission of the publisher. Library of Congress Card Number: 2002103166 ISBN: 0-7821-4047-5 SYBEX and the SYBEX logo are either registered trademarks or trademarks of SYBEX Inc. in the United States and/or other countries.Screen reproductions produced with FullShot 99. FullShot 99 © 1991-1999 Inbit Incorporated. All rights reserved.FullShot is a trademark of Inbit Incorporated. The CD interface was created using Macromedia Director, COPYRIGHT 1994, 1997-1999 Macromedia Inc.
    [Show full text]