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Rosen Navigation uses Global Positioning System (GPS) satellites, and a digital roadway map database to calculate and display travel directions. The system's GPS antenna receives signals from a constellation of 24 satellites orbiting the earth and uses the strongest of signals, to determine your position to within meters. _______________________________________________________ Main Menu Touch to enter Main Menu (Figure 3.). The Main Menu includes TV, Bluetooth, iPodTM, Radio, XM, DVD, AV, Navigation and Setup. Touch the function you would like to select. Figure 3. Before operating your Rosen Navigation system, please carefully read and follow the instructions provided in the "Safety Information and Precautions" located on Page 14 of this Quick Start Guide. _______________________________________________________ For Complete Operating Instructions Please refer to the Rosen Navigation User's Manual to find complete instructions on the operation and many features of the Rosen Navigation System. For detailed information of these functions, please refer to the "User's Manual".
Guna membantu BPBA khususnya pada ruang pencegahan dan kesiap siagaan dalam mengatasi proses pendataan bencana yang terhambat dengan adanya gangguan jaringan internet maupun gangguan server database di kantor pusat, maka penulis merancang dan mengembangkan sebuah perangkat lunak yang membantu dalam melakukan proses pendataan bencana di Provinsi Aceh demi mengantisipasi permasalahan tersebut. Pengembangan perangkat lunak tersebut menggunakan bahasa pemograman java dengan menggunakan Netbeans IDE versi 7.1 dan MySQL Server versi 5.5 sebagai basis data. Produk dari pengembangan tersebut adalah Sistem Informasi Pendataan Bencana (SIPEB). SIPEB merupakan perangkat lunak berbasis aplikasi desktop, yang memiliki media penyimpanan data di dalam sebuah database. Tidak hanya fungsi Create Read Update Delete (CRUD) yang terdapat didalam SIPEB, fungsi lain yang terdapat pada SIPEB yaitu fungsi backup data, restore data, cetak laporan harian, cetak laporan bulanan dan cetak laporan tahunan. Perangkat lunak yang dirancang ini berfungsi untuk melakukan pendataan bencana, penyajian informasi bencana yang pernah terjadi dalam bentuk laporan dan juga pemeliharaan data dari kerusakan data, kehilangan data maupun penggandaan data. Kata Kunci : Badan Penanggulangan Bencana Aceh (BPBA), pencegahan dan kesiapsiagaan, Sistem Informasi Pendataan Bencana (SIPEB). I. gangguan atau permasalahan jaringan internet pada kantor tersebut akibatnya data-data maupun laporan bencana yang harus di input akan menjadi terlambat sehingga menghambat kelancaran aktivitas operator untuk menginput laporan pendataan bencana. Sistem yang dibuat berbasis desktop application menggunakan database sebagai penyimpanan data. Penyimpanan data di dalam database dapat meminimalisir kemungkinan data tersebut hilang atau terkena virus computer yang mengakibatkan data itu rusak.
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Database has to be correct and accurate for convey the marketing massage to the right customer. If not then the marketing message will be wasted if it isn’t received by the right people. Contact us: Easy Leads Pty Ltd, Suite 5, Level 1, 277 Condamine Street, Manly Vale, NSW 2093, Ph: 02 8488 8266, Web: www.easyleads.com.au
In this paper, we examine a number of SQL and socalled “NoSQL” data stores designed to scale simple OLTP-style application loads over many servers. Originally motivated by Web 2.0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses. We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g. database-wide transaction consistency, in order to achieve others, e.g. higher availability and scalability. Note: Bibliographic references for systems are not listed, but URLs for more information can be found in the System References table at the end of this paper. Caveat: Statements in this paper are based on sources and documentation that may not be reliable, and the systems described are “moving targets,” so some statements may be incorrect. Verify through other sources before depending on information here. Nevertheless, we hope this comprehensive survey is useful! Check for future corrections on the author’s web site cattell.net/datastores. Disclosure: The author is on the technical advisory board of Schooner Technologies and has a consulting business advising on scalable databases.
Oracle NoSQL Database and MongoDB server are both licensed under AGPL while MongoDB has certain client drivers under the Apache 2.0 license. Oracle NoSQL Database is in many respects, as a NoSQL Database implementation leveraging BerkeleyDB in its storage layer, a commercialization of the early NoSQL implementations which lead to the adoption of this category of technology. Several of the earliest NoSQL solutions were based on BerkeleyDB and some are still to this day e.g. LinkedIn’s Voldemort. The Oracle NoSQL Database is a Java based key-value store implementation that supports a value abstraction layer currently implementing Binary and JSON types. Its key structure is designed in such a way as to facilitate large scale distribution and storage locality with range based search and retrieval. The implementation uniquely supports built in cluster load balancing and a full range of transaction semantics from ACID to relaxed eventually consistent. In addition, the technology is integrated with important open source technologies like Hadoop / MapReduce, an increasing number of Oracle software solutions and tools and can be found on Oracle Engineered Systems.
It has now been a good couple of years since the various anti-SQL proponents have gained enough momentum to come together under the wide umbrella of the term NoSQL. And it is clear that we can never go back: the typical relational database architecture is clearly insufficient for today’s dataintensive applications, and the move to distributed architectures. But is the problem in the architecture or the query language? The two are not interchangeable, though frequently confused. Some answers can be found in the following articles, which represent a progression of ideas on this very relevant topic, based on various articles published in Nati Shalom’s blog: http://natishalom.typepad.com Should Web Apps "Just Say No" to SQL? Pros and Cons of Non-SQL Patterns This paper briefly reviews what is driving the trend of adopting alternatives to the traditional SQL DB, survey alternative approaches, and discuss not only their benefits but also the risks and caveats for real-life web applications.
As companies deal with ever larger amounts of data and increasingly demanding workloads, a new class of databases has taken hold. Dubbed “NoSQL”, these databases trade some of the features used by traditional relational databases in exchange for increased performance and/or partition tolerance. But as NoSQL solutions have proliferated and differentiated themselves (into key-value stores, document databases, graph databases, and “NewSQL”), trying to evaluate the database landscape for a particular class of problem becomes more and more difficult. In this paper we attempt to answer this question for one specific, but critical, class of functionality – applications that need the highest possible raw performance for a reliable storage engine. There have been a few attempts to provide standardized tools to measure performance or other characteristics, but these have been hobbled by the lack of a clear mandate on exactly what they’re testing, plus an inability to measure load at the highest volumes. In addition, there is an implicit tradeoff between the consistency and durability requirements of an application and the maximum throughput that can be processed. What is needed is not an attempt to quantify every NoSQL solution into one artificial bucket, but a more systemic analysis of how some of these databases can achieve under assumptions that mirror real-world application needs. We attempted to provide a comprehensive answer to one specific set of use cases for NoSQL databases -- consumer-facing applications which require extremely high throughput and low latency, and whose information can be represented using a key-value schema. In particular, we look at two common scenarios.