Chapter 9 – Designing the Database
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What Is a Complex Adaptive System?
PROJECT GUTS What is a Complex Adaptive System? Introduction During the last three decades a leap has been made from the application of computing to help scientists ‘do’ science to the integration of computer science concepts, tools and theorems into the very fabric of science. The modeling of complex adaptive systems (CAS) is an example of such an integration of computer science into the very fabric of science; models of complex systems are used to understand, predict and prevent the most daunting problems we face today; issues such as climate change, loss of biodiversity, energy consumption and virulent disease affect us all. The study of complex adaptive systems, has come to be seen as a scientific frontier, and an increasing ability to interact systematically with highly complex systems that transcend separate disciplines will have a profound affect on future science, engineering and industry as well as in the management of our planet’s resources (Emmott et al., 2006). The name itself, “complex adaptive systems” conjures up images of complicated ideas that might be too difficult for a novice to understand. Instead, the study of CAS does exactly the opposite; it creates a unified method of studying disparate systems that elucidates the processes by which they operate. A complex system is simply a system in which many independent elements or agents interact, leading to emergent outcomes that are often difficult (or impossible) to predict simply by looking at the individual interactions. The “complex” part of CAS refers in fact to the vast interconnectedness of these systems. Using the principles of CAS to study these topics as related disciplines that can be better understood through the application of models, rather than a disparate collection of facts can strengthen learners’ understanding of these topics and prepare them to understand other systems by applying similar methods of analysis (Emmott et al., 2006). -
Dimensions of Ecosystem Complexity: Heterogeneity, Connectivity, and History
ecological complexity 3 (2006) 1–12 available at www.sciencedirect.com journal homepage: http://www.elsevier.com/locate/ecocom Viewpoint Dimensions of ecosystem complexity: Heterogeneity, connectivity, and history M.L. Cadenasso a,*, S.T.A. Pickett b, J.M. Grove c a Hixon Center for Urban Ecology, School of Forestry and Environmental Studies, Yale University, 205 Prospect Street, New Haven, CT 06511, United States b Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, United States c USDA Forest Service, Northeastern Research Station, 705 Spear Street, P.O. Box 968, Burlington, VT 05401, United States article info abstract Article history: Biocomplexity was introduced to most ecologists through the National Science Foundation’s Received 2 June 2005 grant program, and the literature intended to introduce that program. The generalities of that Received in revised form literature contrast with the abstract and mathematical sophistication of literature from 30 June 2005 physics, systems theory, and indeed even of pioneering ecologists who have translated the Accepted 2 July 2005 conceptintoecology. Thissituation leaves a middle ground, that isboth accessibletoecologists Published on line 23 January 2006 in general, and cognizant of the fundamentals of complexity, to be more completely explored. To help scope this middle ground, and to promote empirical explorations that may be located Keywords: there, we propose a non-exclusive framework for the conceptual territory. While recognizing Biocomplexity the deep foundations in the studies of complex behavior, we take ecological structure as the Framework entry point for framework development. This framework is based on a definition of biocom- Coupled systems plexity as the degree to which ecological systems comprising biological, social and physical Spatial heterogeneity components incorporate spatially explicit heterogeneity, organizational connectivity, and Legacies historical contingency through time. -
Schema in Database Sql Server
Schema In Database Sql Server Normie waff her Creon stringendo, she ratten it compunctiously. If Afric or rostrate Jerrie usually files his terrenes shrives wordily or supernaturalized plenarily and quiet, how undistinguished is Sheffy? Warring and Mahdi Morry always roquet impenetrably and barbarizes his boskage. Schema compare tables just how the sys is a table continues to the most out longer function because of the connector will often want to. Roles namely actors in designer slow and target multiple teams together, so forth from sql management. You in sql server, should give you can learn, and execute this is a location of users: a database projects, or more than in. Your sql is that the view to view of my data sources with the correct. Dive into the host, which objects such a set of lock a server database schema in sql server instance of tables under the need? While viewing data in sql server database to use of microseconds past midnight. Is sql server is sql schema database server in normal circumstances but it to use. You effectively structure of the sql database objects have used to it allows our policy via js. Represents table schema in comparing new database. Dml statement as schema in database sql server functions, and so here! More in sql server books online schema of the database operator with sql server connector are not a new york, with that object you will need. This in schemas and history topic names are used to assist reporting from. Sql schema table as views should clarify log reading from synonyms in advance so that is to add this game reports are. -
Database-Centric Programming for Wide-Area Sensor Systems
Database-Centric Programming for Wide-Area Sensor Systems 1 2 1 2 Shimin Chen , Phillip B. Gibbons , and Suman Nath ; 1 Carnegie Mellon University fchensm,[email protected] 2 Intel Research Pittsburgh [email protected] Abstract. A wide-area sensor system is a complex, dynamic, resource-rich col- lection of Internet-connected sensing devices. In this paper, we propose X-Tree Programming, a novel database-centric programming model for wide-area sen- sor systems designed to achieve the seemingly conflicting goals of expressive- ness, ease of programming, and efficient distributed execution. To demonstrate the effectiveness of X-Tree Programming in achieving these goals, we have in- corporated the model into IrisNet, a shared infrastructure for wide-area sensing, and developed several widely different applications, including a distributed in- frastructure monitor running on 473 machines worldwide. 1 Introduction A wide-area sensor system [2, 12, 15, 16] is a complex, dynamic, resource-rich collec- tion of Internet-connected sensing devices. These devices are capable of collecting high bit-rate data from powerful sensors such as cameras, microphones, infrared detectors, RFID readers, and vibration sensors, and performing collaborative computation on the data. A sensor system can be programmed to provide useful sensing services that com- bine traditional data sources with tens to millions of live sensor feeds. An example of such a service is a Person Finder, which uses cameras or smart badges to track people and supports queries for a person's current location. A desirable approach for develop- ing such a service is to program the collection of sensors as a whole, rather than writing software to drive individual devices. -
Referential Integrity in Sqlite
CS 564: Database Management Systems University of Wisconsin - Madison, Fall 2017 Referential Integrity in SQLite Declaring Referential Integrity (Foreign Key) Constraints Foreign key constraints are used to check referential integrity between tables in a database. Consider, for example, the following two tables: create table Residence ( nameVARCHARPRIMARY KEY, capacityINT ); create table Student ( idINTPRIMARY KEY, firstNameVARCHAR, lastNameVARCHAR, residenceVARCHAR ); We can enforce the constraint that a Student’s residence actually exists by making Student.residence a foreign key that refers to Residence.name. SQLite lets you specify this relationship in several different ways: create table Residence ( nameVARCHARPRIMARY KEY, capacityINT ); create table Student ( idINTPRIMARY KEY, firstNameVARCHAR, lastNameVARCHAR, residenceVARCHAR, FOREIGNKEY(residence) REFERENCES Residence(name) ); or create table Residence ( nameVARCHARPRIMARY KEY, capacityINT ); create table Student ( idINTPRIMARY KEY, firstNameVARCHAR, lastNameVARCHAR, residenceVARCHAR REFERENCES Residence(name) ); or create table Residence ( nameVARCHARPRIMARY KEY, 1 capacityINT ); create table Student ( idINTPRIMARY KEY, firstNameVARCHAR, lastNameVARCHAR, residenceVARCHAR REFERENCES Residence-- Implicitly references the primary key of the Residence table. ); All three forms are valid syntax for specifying the same constraint. Constraint Enforcement There are a number of important things about how referential integrity and foreign keys are handled in SQLite: • The attribute(s) referenced by a foreign key constraint (i.e. Residence.name in the example above) must be declared UNIQUE or as the PRIMARY KEY within their table, but this requirement is checked at run-time, not when constraints are declared. For example, if Residence.name had not been declared as the PRIMARY KEY of its table (or as UNIQUE), the FOREIGN KEY declarations above would still be permitted, but inserting into the Student table would always yield an error. -
Form Follows Function Model-Driven Engineering for Clinical Trials
Form Follows Function Model-Driven Engineering for Clinical Trials Jim Davies1, Jeremy Gibbons1, Radu Calinescu2, Charles Crichton1, Steve Harris1, and Andrew Tsui1 1 Department of Computer Science, University of Oxford Wolfson Building, Parks Road, Oxford OX1 3QD, UK http://www.cs.ox.ac.uk/firstname.lastname/ 2 Computer Science Research Group, Aston University Aston Triangle, Birmingham B4 7ET, UK http://www-users.aston.ac.uk/~calinerc/ Abstract. We argue that, for certain constrained domains, elaborate model transformation technologies|implemented from scratch in general- purpose programming languages|are unnecessary for model-driven en- gineering; instead, lightweight configuration of commercial off-the-shelf productivity tools suffices. In particular, in the CancerGrid project, we have been developing model-driven techniques for the generation of soft- ware tools to support clinical trials. A domain metamodel captures the community's best practice in trial design. A scientist authors a trial pro- tocol, modelling their trial by instantiating the metamodel; customized software artifacts to support trial execution are generated automati- cally from the scientist's model. The metamodel is expressed as an XML Schema, in such a way that it can be instantiated by completing a form to generate a conformant XML document. The same process works at a second level for trial execution: among the artifacts generated from the protocol are models of the data to be collected, and the clinician conduct- ing the trial instantiates such models in reporting observations|again by completing a form to create a conformant XML document, represent- ing the data gathered during that observation. Simple standard form management tools are all that is needed. -
2. Creating a Database Designing the Database Schema
2. Creating a database Designing the database schema ..................................................................................... 1 Representing Classes, Attributes and Objects ............................................................. 2 Data types .......................................................................................................................... 5 Additional constraints ...................................................................................................... 6 Choosing the right fields ................................................................................................. 7 Implementing a table in SQL ........................................................................................... 7 Inserting data into a table ................................................................................................ 8 Primary keys .................................................................................................................... 10 Representing relationships ........................................................................................... 12 Altering a table ................................................................................................................ 22 Designing the database schema As you have seen, once the data model for a system has been designed, you need to work out how to represent that model in a relational database. This representation is sometimes referred to as the database schema. In a relational database, the schema defines -
The Unconstrained Primary Key
IBM Systems Lab Services and Training The Unconstrained Primary Key Dan Cruikshank www.ibm.com/systems/services/labservices © 2009 IBM Corporation In this presentation I build upon the concepts that were presented in my article “The Keys to the Kingdom”. I will discuss how primary and unique keys can be utilized for something other than just RI. In essence, it is about laying the foundation for data centric programming. I hope to convey that by establishing some basic rules the database developer can obtain reasonable performance. The title is an oxymoron, in essence a Primary Key is a constraint, but it is a constraint that gives the database developer more freedom to utilize an extremely powerful relational database management system, what we call DB2 for i. 1 IBM Systems Lab Services and Training Agenda Keys to the Kingdom Exploiting the Primary Key Pagination with ROW_NUMBER Column Ordering Summary 2 www.ibm.com/systems/services/labservices © 2009 IBM Corporation I will review the concepts I introduced in the article “The Keys to the Kingdom” published in the Centerfield. I think this was the inspiration for the picture. I offered a picture of me sitting on the throne, but that was rejected. I will follow this with a discussion on using the primary key as a means for creating peer or subset tables for the purpose of including or excluding rows in a result set. The ROW_NUMBER function is part of the OLAP support functions introduced in 5.4. Here I provide some examples of using ROW_NUMBER with the BETWEEN predicate in order paginate a result set. -
Environmental Systems Analysis Tools for Decision-Making
Environmental systems analysis tools for decision-making LCA and Swedish waste management as an example Åsa Moberg Licentiate thesis Royal Institute of Technology Department of Urban Planning and Environment Environmental Strategies Research Stockholm 2006 Titel: Environmental systems analysis tools for decision‐making LCA and Swedish waste management as an example Author: Åsa Moberg Cover page photo: Marianne Lockner TRITA‐SOM 06‐002 ISSN 1653‐6126 ISRN KTH/SOM/‐‐06/002‐‐SE ISBN 91‐7178‐304‐0 Printed in Sweden by US AB, Stockholm, 2006 2 Abstract Decisions are made based on information of different kinds. Several tools have been developed to facilitate the inclusion of environmental aspects in decision‐making on different levels. Which tool to use in a specific decision‐making situation depends on the decision context. This thesis discusses the choice between different environmental systems analysis (ESA) tools and suggests that key factors influencing the choice of ESA tool are object of study, impacts considered and information type regarding site‐specificity and according to the DPSIR‐framework. Waste management in Sweden is used as an example to illustrate decision‐making situations, but discussions concerning choice of tools are also thought to be of general concern. It is suggested that there is a need for a number of ESA tools in waste management decision‐making. Procedural tools like Environmental Impact Assessment (EIA) and Strategic Environmental Assessment (SEA) should be used e.g. by companies applying for development of waste management facilities and by public authorities preparing plans and programmes. Within these procedural tools analytical tools providing relevant information could be used, e.g. -
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle
A Quantitative Reliability, Maintainability and Supportability Approach for NASA's Second Generation Reusable Launch Vehicle Fayssai M. Safie, Ph. D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Fayssal.Safie @ msfc.nasa.gov Charles Daniel, Ph.D. Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-5278 E-mail: Charles.Daniel @msfc.nasa.gov Prince Kalia Raytheon ITSS Marshall Space Flight Center Huntsville, Alabama Tel: 256-544-6871 E-mail: Prince.Kalia @ msfc.nasa.gov ABSTRACT The United States National Aeronautics and Space Administration (NASA) is in the midst of a 10-year Second Generation Reusable Launch Vehicle (RLV) program to improve its space transportation capabilities for both cargo and crewed missions. The objectives of the program are to: significantly increase safety and reliability, reduce the cost of accessing low-earth orbit, attempt to leverage commercial launch capabilities, and provide a growth path for manned space exploration. The safety, reliability and life cycle cost of the next generation vehicles are major concerns, and NASA aims to achieve orders of magnitude improvement in these areas. To get these significant improvements, requires a rigorous process that addresses Reliability, Maintainability and Supportability (RMS) and safety through all the phases of the life cycle of the program. This paper discusses the RMS process being implemented for the Second Generation RLV program. 1.0 INTRODUCTION The 2nd Generation RLV program has in place quantitative Level-I RMS, and cost requirements [Ref 1] as shown in Table 1, a paradigm shift from the Space Shuttle program. This paradigm shift is generating a change in how space flight system design is approached. -
Keys Are, As Their Name Suggests, a Key Part of a Relational Database
The key is defined as the column or attribute of the database table. For example if a table has id, name and address as the column names then each one is known as the key for that table. We can also say that the table has 3 keys as id, name and address. The keys are also used to identify each record in the database table . Primary Key:- • Every database table should have one or more columns designated as the primary key . The value this key holds should be unique for each record in the database. For example, assume we have a table called Employees (SSN- social security No) that contains personnel information for every employee in our firm. We’ need to select an appropriate primary key that would uniquely identify each employee. Primary Key • The primary key must contain unique values, must never be null and uniquely identify each record in the table. • As an example, a student id might be a primary key in a student table, a department code in a table of all departments in an organisation. Unique Key • The UNIQUE constraint uniquely identifies each record in a database table. • Allows Null value. But only one Null value. • A table can have more than one UNIQUE Key Column[s] • A table can have multiple unique keys Differences between Primary Key and Unique Key: • Primary Key 1. A primary key cannot allow null (a primary key cannot be defined on columns that allow nulls). 2. Each table can have only one primary key. • Unique Key 1. A unique key can allow null (a unique key can be defined on columns that allow nulls.) 2. -
MDA Process to Extract the Data Model from Document-Oriented Nosql Database
MDA Process to Extract the Data Model from Document-oriented NoSQL Database Amal Ait Brahim, Rabah Tighilt Ferhat and Gilles Zurfluh Toulouse Institute of Computer Science Research (IRIT), Toulouse Capitole University, Toulouse, France Keywords: Big Data, NoSQL, Model Extraction, Schema Less, MDA, QVT. Abstract: In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property "schema less" which means absence of the data model when creating a database. This property brings an undeniable flexibility by allowing the evolution of the model during the exploitation of the base. However, query expression requires a precise knowledge of the data model. In this article, we propose a process to automatically extract the physical model from a document- oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From a NoSQL database, we propose formal transformation rules with QVT to generate the physical model. An experimentation of the extraction process was performed on the case of a medical application. 1 INTRODUCTION however that it is absent in some systems such as Cassandra and Riak TS. The "schema less" property Recently, there has been an explosion of data offers undeniable flexibility by allowing the model to generated and accumulated by more and more evolve easily. For example, the addition of new numerous and diversified computing devices. attributes in an existing line is done without Databases thus constituted are designated by the modifying the other lines of the same type previously expression "Big Data" and are characterized by the stored; something that is not possible with relational so-called "3V" rule (Chen, 2014).