Only the data that suits the nature of the problem being solved should be gathered. The main specific strategic actions, out of which, 23 are proposed by Member States aim for a "centred, sustainable and inclusive" growth of the European economy. When we talk about long-term storage or long-term preservation of data in an university environment, we also have to talk about the records, produced as the result of the administrative processes. Video and other media files have greatly contributed to the growth of databases. Compania sprijină pe deplin dezvoltarea și succesul brandurilor sale Kamis și Galeo. Sunil Maheshwari was the sources of many encouraging conversations about it.
Eine kleine Enzyklopädie der digitalen Langzeitarchivierung. Rothenberg, Jeff. Avoiding technological quicksand. Finding a viable technical foundation for digital preservation.
Smith, Vincent S. Towards a database of everything. There are analyzed the types of specific activities and the manner in which university libraries should respond, so as to support the transposition in practice of the strategic directions i A series of new services are proposed, based on the concept Future Internet wherewith a university library may contribute to the education process, in order to support the development of the cohesion education research innovation.
Key conquer online dating un ghid consultant de strategie European strategy i, digital content, Future Internet, university libraries, service. The paper summarises the actions proposed by the i European Strategy regarding the development of digital content in various areas.
Specific conquer online dating un ghid consultant de strategie of activities are analysed along with the manner university libraries should respond in order to implement the strategic directions of i The paper presents a series of new services based on the "Future Internet" concept, services that allow an university library to contribute to the educational process supporting the development of education - research - innovation cohesion. Two elements that are also defining the i have played a key role in the European programmes and strategies: Digital Literacy and avoiding of the "digital divide"; Development of digital content.
Along with these two components, in the last years of the past decade a new approach was also added, namely the electronic services e-services. The i Digital Agenda The Digital Agenda launched in September has the following strategic objectives: - a unique and dynamic digital market; - interoperability and standardisation; - reliability and security; - fast and super-fast Internet access; - research and innovation; - increase the degree of Digital Literacy, development of digital proficiency and inclusion; - social advantages of ICT in the European Union.
As it can be noticed, among these objectives, Internet access and Digital Literacy are directly oriented towards the citizen and implicitly towards electronic services. The main specific strategic actions, out of which, 23 are proposed by Member States aim for a "centred, sustainable and inclusive" growth of the European economy.
Informatics in the 21st Century Trends At the end of the 20th Century, informatics was profoundly marked by the outburst of the PCs, software applications developed on this platform and spread in all domains of activity. Besides, libraries, in the ample modernisation movement, have adopted the same general view. In the 21st Century, informatics seems to follow other directions, presented below: - shifting from PC informatics to general platforms made available to several users simultaneously; - development of complex hardware and software infrastructures, such as Grid, Cloud, etc.
In the last decade, the library sector was strongly influenced by concepts and activities concerning document digitisation. Financing of numerous research projects on national and European level oriented towards the "digital library" concept, in many countries, as well as European documents concerning the establishment of national digital libraries led to the emergence of numerous digital collections.
These are created in libraries as well as in other structures that don't necessarily have library functions but are employed in the process of information-documentation for various users' communities, researchers' communities, public employees, etc. The emergence of digital collections implicitly led to the necessity of collaboration and cooperation on institutional and functional levels, directly affecting the services utilised.
In this respect Europeana Europeana Digital Library is significant. The existence of the digital content, sometimes its overlapping, brought to life new concepts such as users' management, digital collection management, etc. These services can be found in a larger or smaller extend in computerized libraries with digital collections backed by an suitable software instrument.
The fundamental difference is the shifting from a service oriented towards library to a service oriented towards Web, sustained by suitable communication networks. In a broader approach, this kind of services can be stored on CLOOUD type platforms, available for libraries and implicitly for users. In this respect the transition from the digital library to the virtual one becomes obvious.
The elements that define these two concepts are presented in the illustration below: Digital library collection : o repository of digital documents and information managed with ICT instruments; o High performance computing resources made available for specific functions semantic search, automated classification and tabulation of information, automated keyword and knowledge extraction ; Virtual Library: o Shared repository of knowledges, physically dematerialized; 10 24 International Conference on Library and Information Science BIBLIO o virtual organisation administering digital and informatics resources for a community ensures the management of information, documents and users ; o collaborative environment for education and scientific research.
Cerbung partea 19 libraries have all necessary elements for the development of content-based e- Services. The main arguments are: - numerous users that can also produce quality, up-to-date content in various areas professors and researchers activating in universities ; - the current trend in education of promoting e-learning systems; - suitable ICT infrastructures present in all universities high-speed networks, equipment, Grid platforms, Cloud platforms, etc.
On the other hand, objectives of universities related to the development of ICT abilities for students, along with study support on electronic media, concur in a definite manner to the emergence of procedures strictly related to e-services. The schematic diagram of the operation-oriented e-services platform for Libraries is described as fallows: The functional scheme of the service platform for libraries Conclusions Creating hardware and software platforms to support e-services within a library may be a new approach towards modernisation of libraries.
Achieving such a model to comply with the present information trends involves a significant effort for organisation, standardisation and cooperation.
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Equally, the interoperability of library systems must have a central role in the strategy and the policy of creating a services platform for libraries. This implies the transition from "raw data" to the computerised service, thus e-services.
Banciu, Doina, Năstase, Pavel. În: Computerworld, martiepag Banciu, Doina. Digital Libraries from Concept to Practice. The presentation of the Romanian network is followed by a study structured on four investigation directions: general information about the EDCs, staff and IT equipment; analysis of the websites; analysis of the collections and informational behaviour shown by the users; organisation of access to information and collaboration in the EDC.
Benefiting from the support of the Representation of the EC in Bucharest, these structures, integrated in the Europe Direct Network, ensure the communication of the priorities presented by the EU institutions and their development areas are subordinate to this mission. Key words: European Union, European Documentation Centre, info-documentary structure, information resources, survey. Introduction was an exceptional year that witnessed the accession of Romania in the EU.
The EDC mission is to grow, organize, process, enrich and host collections of books, serials, articles, electronic publications in the EU field, as well as to create the informational environment in order to support the educational and research activities about EU for the Romanian university community. In keeping with this trend towards greater openness, the Library makes its collections accessible to non-specific users - the business community, high-school economy teachers and lawyers, economists - people with an interest in learning about the impact cricket online dating the process of Community integration.
The aim of this paper is to present some results from research undertaken into the informational behavior of these new info-documentary structures users. Furthermore, data 27 BIBLIO International Conference on Library and Information Science from this research will be used in a future study to make a comparison between the preferences and the way of documentation of the Romanian customers and the users in countries where the first centres were established since the early 60s.
Romanian network presentation The evolution of the network meant 12 centres founded inas an effect of the application forms sent to the European Commission Representation in Bucharest; intwo other centres got successful admission; finally, has marked the entry of the most recent 2 new members in the Conquer online dating un ghid consultant de strategie EDCs family.
This is a proof of our pro-european orientation and redefining the libraries and universities according to the information needs of the 21st century new customer.
In terms of network architecture, 13 centres are operating in public and private universities, 2 centres are hosted and coordinated by Central University Libraries and 1 centre in a Chamber of Commerce and Industry.
This diversity of structures which have chosen to integrate an EDC renders the specificity of the Romanian network, taking into account the fact that on European level this kind of centres are run in the universities. The analysis of the map with the geographical position of the network figure 1 highlights the equilibrium as regards the disposal on the territory as well as the fact that the important universities host this kind of centres, with a few exceptions.
Among the tools and services provided by an EDC we mention: 1. Assistance and training to use resources and for information retrieval; 3. Figure 1. For example, the students from the Faculty of Chemistry and Biochemistry are interested, while elaborating Toxicology papers, in European legislation and policies related to this subject i.
Contamination via chemical products. Typically, the documentation and information resources owned by an EDC provide both high level documentation and general information.
The first category includes books and periodicals printed, electronic CDs, DVDs, and online databases, and the second - brochures, posters, flyers distributed free of charge by Publications Office. Even more, when there is organized an event about the communication priorities of the European institutions, the EDCs have the possibility to order free of charge from the bookshop, as privileged partners, publications which would support that particular activity.
Considering the diversity of the host-structures as well as the different degree of training of the staff, in there were organized Training sessions and specialization courses for the professionals involved in collection management from the European documentation centres. It is remarkable and at the same time an exemple to be followed by all the employers in the Romanian system of libraries the fact that the Conquer online dating un ghid consultant de strategie focusses on the human factor in the network, the one which actually ensures descentralised communication, localised debates, in other words the fulfilment of the challenges faced by the Directorate-General Communication of the European Commission.
The communication with the users is achieved by means of EDCs web pages i. European Documentation Centres questions and answers In order to increase the efficiency of these structures - integrated or not in the traditional info-documentary structures librariesbut also to assess how they found their specific place in the Romanian market of the specialized information providers, we conducted a study among the coordinators of the centers, during the second semester ofcompleted during February The conception of such a study was so much more necessary as the literature in the field included an extremely small number of analyses regarding the efficiency of these structures and their impact on the target population.
Even more, the most recent study , expanded on the level of the whole EU does not include Romania, as it was accomplished during the first half ofwhen these CDEs were not present in the newly European member. This survey was administered to the coordinators of the Romanian EDCs and follows the same line as undertaken by the managers from the other European countries, as it is structured on four investigation directions: general information about the EDC, staff and IT equipment; analysis of the website; analysis of the collections and informational behaviour shown by the users; organisation of access to information and collaboration in the EDC Data referring to the duration in the network, type of EDC, position, staff, IT equipment 15 29 BIBLIO International Conference on Library and Information Science Out of the total number of respondents, 1 EDC was founded in the same year as the accession of Romania to EUbut most 7 EDCs signed the agreement with the CE Representation in ; the years and brought 2 more similar centres in the network.
As regards the type of organisation which hosts the EDC, 10 of the given answers mention that the EDCs are hosted by universities 8 public and 2 private universities and 2 belong to different structures 1 Central University Library, 1 Chamber of Commerce.
The alternative would have been the selection of certain fields, which would have meant that the publications received from the Office of Publications covered only those fields. All the coordinators who participated in the survey considered that the space offered to the centre answers the demands for a new and modern information centre, but the number of seats available for study varies from 1 or 2 to a few dozens a maximum of As it could have been foreseen, the greatest number of places is offered to the users by the CDEs included in the great libraries, whose space destined to study is larger.
The analysis of these data could make us draw certain conclusions about the concern to make available for the users a database with all the bibliographic records for the collection of publications.
The presence of Intranet in the EDCs 3. Sloganul companiei este Pentru o viață mai bună. Ungheni, str. Națională or. Călărași, str.
E-nformation S. C est une démarche nécessaire, mais pas suffisante pour le développement durable des structures info documentaires, étant donné que les valeurs et les convictions des bibliothécaires, plutôt traditionnelles et fermement enracinées, limitent la réactivité au changement et à l'innovation.
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Data could come from machines reporting their own status or from logs of web usage. Data can come in many ways. It may come as paper reports. It may come as a file stored on a computer. It may be words spoken over the phone. It may be e-mail or chat on the Internet. It may come as movies and songs in DVDs, and so on. There is also data about data. It is called metadata. For example, people regularly upload videos on YouTube. The format of the video file whether it was a high-def file or lower resolution is metadata.
The information about the time of uploading is metadata.
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The account from which it was uploaded is also metadata. The record of downloads of the 18 video is also metadata. Data can be of different types. Data could be an unordered collection of values. For example, a retailer sells shirts of red, blue, and green colors. There is no intrinsic ordering among these color values. One can hardly argue that any one color is higher or lower than the other. This is called nominal means names data.
Data could be ordered values like small, medium and large. For example, the sizes of shirts could be extra-small, small, medium, and large. There is clarity that medium is bigger than small, and large is bigger than medium.
But the differences may not be equal. This is called ordinal ordered data. Another type of data has discrete numeric values defined in a certain range, with the assumption of equal distance between the values. Customer satisfaction score may be ranked on a point scale with 1 being lowest and 10 being highest.
This requires the respondent to carefully calibrate the entire range as objectively as possible and place his own measurement in that scale. This is called interval equal intervals data. The highest level of numeric data is ratio data which can take on any numeric value. The weights and heights of all employees would be exact numeric values. The price of a shirt will also take any numeric value.
It is called ratio any fraction data. There is another kind of data that does not lend itself to much mathematical analysis, at least not directly. Such data needs to be first structured and then analyzed. These kinds of data lend themselves to different forms of analysis and mining. Songs can be described as happy or sad, fast-paced or slow, and so on. They may contain sentiment and intention, but these are not quantitatively precise.
The precision of analysis increases as data becomes more numeric. Ratio data could be subjected to rigorous mathematical analysis. For example, precise weather data about temperature, pressure, and humidity can be used to create rigorous mathematical models that can accurately predict future weather.
Data may be publicly available and sharable, or it may be marked private. There is a big debate on whether the personal data shared on social media conversations is private or can be used for commercial purposes. Datafication is a new term that means that almost every phenomenon is now being observed and stored.
More devices are connected to the Internet. Every click on the web, and every movement of irak dating vama mobile devices, is being recorded. Machines are generating data. All of this is generating an exponentially growing volume of data, at high velocity.
As storage costs keep coming down at a rapid rate, there is a greater incentive to record and store more events and activities at a higher resolution.
Data is getting stored in more detailed resolution, and many more variables are being captured and stored. Database A database is a modeled collection of data that is accessible in many ways. A data model can be designed to integrate the operational data of the organization. The data model abstracts the key entities involved in an action and their relationships. Most databases today follow the relational data model and its variants. Each data modeling technique imposes rigorous rules and constraints to ensure the integrity and consistency of data over time.
Take the example of a sales organization. A data model for managing customer orders will involve data about customers, orders, products, and their interrelationships. The relationship between the customers and orders would be such that one customer can place many orders, but one order will be placed by one and only one customer. It is called a one-to-many relationship. The relationship between orders and products conquer online dating un ghid consultant de strategie a little more complex.
One order may contain many products. And one product may be contained in many different orders. This is called a many-to-many relationship. Different types of relationships can be modeled in a database. Databases have grown tremendously over time. They have grown in complexity in terms of number of the objects and their properties being recorded. They have also grown in the quantity of data being stored.
A decade ago, a terabyte-sized database was considered big. Today databases are in petabytes and exabytes.
Video and other media files have greatly contributed to the growth of databases. E-commerce and other web-based activities also generate huge amounts of data. Data generated through social media has also generated large databases. The e-mail archives, including attached documents 20 of organizations, are in similar large sizes. Many database management software systems DBMSs are available to help store and manage this data.
These include commercial systems, such as Oracle and DB2 system. These DBMSs help process and store millions of transactions worth of data every second. Here is a simple database of the sales of movies worldwide for a retail organization. It shows sales transactions of movies over three quarters. Using such a file, data can be added, accessed, and updated as needed.
Data can be extracted from operational database to answer a particular set of queries. This data, combined with other data, can be rolled up to a consistent granularity and uploaded to a separate data store called the data warehouse. Therefore, the data warehouse is a simpler version of the operational data base, with the purpose of addressing reporting and decision-making needs only.
The data in the warehouse cumulatively grows as more operational data becomes available and is extracted and appended to the data warehouse. Unlike in the operational database, the data values in the warehouse are not updated. To create a simple data warehouse for the movies sales data, assume a simple objective of tracking sales of movies and making decisions about managing inventory. In creating this data warehouse, all the sales transaction data will be extracted from the operational data files.
The data will be rolled up for all combinations of time period and product number. Thus, there will be one row for every combination of time period and product.
The resulting data warehouse will look like the table that follows. The data warehouse could have been designed at a lower or higher level of detail, or granularity. If the data warehouse were designed on a monthly level, instead of a quarterly level, there would be many more rows of data.
When the number of transactions approaches millions and higher, with dozens of attributes in each transaction, the data warehouse can be large and rich with potential insights. One can then mine the data slice and dice in many different ways and discover unique meaningful patterns.
Aggregating the data helps improve the speed of analysis. A separate data warehouse allows analysis to go on separately in parallel, without burdening the operational database systems Table 1.
Function Database Data stored in databases can be Purpose used for many purposes including day-to-day operations Highly granular data including Granularity all activity and transaction details Highly complex with dozens or Complexity hundreds of data files, linked through common data fields Database grows with growing 23 Data Warehouse Data stored in DW is cleansed data useful for reporting and analysis Lower granularity data; rolled up to certain key dimensions of interest Typically organized around a large fact tables, and many lookup tables Grows as data from Size volumes of activity and operational databases is transactions.
Old completed rolled-up and appended transactions are deleted to reduce every day. Data is retained size. There is a wide variety of patterns that can be found in the data. There are many techniques, simple or complex, that help with finding patterns. In this example, a simple data analysis technique can be applied to the data in the data warehouse above. A simple cross-tabulation of results by quarter and products will reveal some easily visible patterns. What is the best selling movie by revenue?
What is the best quarter by revenue this year? Any other patterns?
These simple insights can help plan marketing promotions and manage inventory of various movies. If a cross tabulation was designed to include customer location data, one could answer other questions, such as 1. What is the best selling geography? What is the worst selling geography?
If the data mining was done at the monthly level of data, it would be easy to miss the seasonality of the movies. However, one would have observed that September is the highest selling month.
The previous example shows that many differences and patterns can be noticed by analyzing data in different ways. However, some insights are more important than others. The value of the insight depends upon the problem being solved. The insight that there are more sales of a product in a certain quarter helps a manager plan what products to focus on. In this case, the store manager should stock up on Matrix in Quarter 3 Q3.
Similarly, knowing which quarter has the highest overall sales allows for different resource decisions in that quarter. In this case, if Q3 is bringing more than half of total sales, this requires greater attention on the e-commerce website in the third quarter. Data mining should be done to solve high-priority, high-value problems. Much effort is required to gather data, clean and organize it, mine it with many techniques, interpret the results, and find the right insight.
It is important that there be a large expected payoff from finding the insight. One should select the right data and ignore the restorganize it into a nice and imaginative framework that brings relevant data together, and then apply data mining techniques to deduce the right insight. Data can be analyzed at multiple levels of granularity and could lead to a large number of interesting combinations of data and interesting patterns.
Some of the patterns may be more meaningful than the others. Such highly granular data is often used, especially in finance and high-tech areas, so that one can gain even the slightest edge over the competition. Here are brief descriptions of some of the most important data mining techniques used to generate insights from data. Decision Trees: They help classify populations into classes.
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Thus, decision trees are the most popular and important data mining technique. There are many popular algorithms to make decision trees. They differ in terms of their mechanisms and each technique work well for different situations.
It is possible to try multiple decision-tree algorithms on a data set and compare the predictive accuracy of each tree. Regression: This is a well-understood technique from the field of statistics. The goal is to find a best fitting curve through the many data points.
The best fitting curve is that which minimizes the error distance between the actual data points and the values predicted by the curve.
Regression models can be projected into the future for prediction and forecasting purposes. Artificial Neural Networks: Originating in the field of artificial intelligence and machine learning, ANNs are multi-layer non-linear information processing models that learn from past data and predict future values.
These models predict well, leading to their popularity. Thus, neural networks are opaque like a black-box. These systems also require a large amount of past data to adequate train the system. Cluster analysis: This is an important data mining technique for dividing and conquering large data sets.
The data set is divided into a certain number of clusters, by discerning similarities and dissimilarities within the data. There is no one right answer for the number of clusters in the data. The user needs to make a decision by looking at how well the number of clusters chosen fit the data. This is most commonly used for market segmentation. Unlike decision 26 trees and regression, there is no one right answer for cluster analysis. Association Rule Mining: Also called Market Basket Analysis when used in retail industry, these techniques look for associations between data values.
An analysis of items frequently found together in a market basket can help crosssell products, and also create product bundles. Data Visualization As data and insights grow in number, a new requirement is the ability of the executives and decision makers to absorb this information in real time.
There is a limit to human comprehension and visualization capacity. That is a good reason to prioritize and manage with fewer but key variables that relate directly to the Key Result Areas KRAs of a role. Here are few considerations when presenting using data: 1.
Present the conclusions and not just report the data. Choose wisely from a palette of graphs to suit the data. Organize the results to make the central point stand out.
Ensure that the visuals accurately reflect the numbers. Inappropriate visuals can create misinterpretations and funny dating comics. Make the presentation unique, imaginative and memorable. Executive dashboards are designed to provide information on select few variables for every executive.
They use graphs, dials, and lists to show the status of important parameters. These dashboards also have a drill-down capability to enable a root-cause analysis of exception situations Figure 1. Many dimensions of data can be effectively displayed on a two-dimensional surface to give a rich and more insightful description of the totality of the story.
It covers about six dimensions. Time is on horizontal axis. The geographical coordinates and rivers are mapped in. The thickness of the bar shows the number of troops at any point of time that is mapped. One color is used for the onward march and another for the retreat. The weather temperature at each time is shown in the line graph at the bottom. The rest of the book can be considered in three sections. Section 1 will cover high level topics. Chapter 2 will cover the field of business intelligence and its applications across industries and functions.
Chapter 3 will briefly explain what is data warehousing and how does it help with data mining. Chapter 4 will then describe data mining in some detail with an overview of its major tools and techniques.
Section 2 is focused on data mining techniques. Every technique will be shown through solving an conquer online dating un ghid consultant de strategie in details. Chapter 5 will show the power and ease of decision trees, which are the most popular data mining technique. Chapter 6 will describe statistical regression modeling techniques. Chapter 7 will provide an overview of artificial neural networks, a versatile machine learning technique. Chapter 8 will describe how Cluster Analysis can help with market segmentation.
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Finally, chapter 9 will describe the Association Rule Mining technique, also called Market Basket Analysis, that helps finds shopping patterns. Section 3 will cover more advanced new topics. Chapter 10 will introduce the concepts and techniques of Text Mining, that helps discover insights from text data including social media data.
Chapter 11 will cover provide an overview of the growing field of web mining, which includes mining the structure, content and usage of web sites. Chapter 12 will provide an overview of the recent field of Big Data. Chapter 13 has been added as a primer on Data Modeling, for those who do not have any background in databases, and should be used if necessary.
Which of these would be relevant in your current work? How does it help? Chapter 2 will cover business intelligence concepts, and its applications in many industries.
Chapter 3 will describe data warehousing systems, and ways of creating and managing them. Chapter 5 will describe data visualization as a whole, with techniques and examples, and with many thumb rules of effective data visualizations. Figure 2. Information is the life-blood of business. Businesses use many techniques for understanding their environment and predicting the future for their own benefit and growth. Decisions are made from facts and feelings.
Data-based decisions are more effective than those based on feelings alone. Actions based on accurate satanist dating site, information, knowledge, experimentation, and testing, using fresh insights, can more likely succeed and lead to sustained growth.
Therefore, organizations should gather data, sift through it, analyze and mine it, find insights, and then embed those insights into their operating procedures. There is a new sense of importance and urgency around data as it is being viewed as a new natural resource. It can be mined for value, insights, and competitive advantage.
In a hyperconnected world, where everything is potentially connected to everything else, with potentially infinite correlations, data represents the impulses of nature in the form of certain events and attributes. A skilled business person is motivated to use this cache of data to harness nature, and to find new niches of unserved opportunities that could become profitable ventures. It provides short YouTube based video lessons on thousands of topics for free.
It shot into prominence when Bill Gates promoted it as a resource that he used to teach his own children. With this kind of a resource classrooms are being flipped … i. Students can access the lessons at any time to learn at their own pace. Khan Academy has developed tools to help teachers get a pulse on what's happening in the classroom. Teachers are provided a set of real-time dashboards to give them information from the macro level "How is my class doing on geometry?
Q2: Design a dashboard for tracking your own career. Risk is the result of a probabilistic world where there are no certainties and complexities abound. People use crystal balls, astrology, palmistry, ground hogs, and also mathematics conquer online dating un ghid consultant de strategie numbers to mitigate risk in decision-making.
The goal is to make effective decisions, while reducing risk. Businesses calculate risks and make decisions based on a broad set of facts and insights. Reliable knowledge about the future can help managers make the right decisions with lower levels of risk. The speed of action has risen exponentially with the growth of the Internet. In a hypercompetitive world, the speed of a decision and the consequent action can be a key advantage.
The Internet and mobile technologies allow decisions to be made anytime, anywhere. Research has shown that an unfavorable comment about the company and its products on social media should not go unaddressed for long. On the other hand, a positive sentiment expressed on social media should also be utilized as a potential sales and promotion opportunity, while the opportunity lasts.
BI can help make both better.
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Strategic decisions are those that impact the direction of the company. The decision to reach out to a new customer set would be a strategic decision. Operational decisions are more routine and tactical decisions, focused on developing greater efficiency.
Updating an old website with new features will be an operational decision.
NATO-ÎNTRE SUCCESUL TRANSFORMĂRII ŞI TRANSFORMAREA SUCCESULUI…
In strategic decision-making, the goal itself may or may not be clear, and the same is true for the path to reach the goal. The consequences of the decision would be apparent some time later.
Thus, one is constantly scanning for new possibilities and new paths to achieve the goals. BI can help with what-if analysis of many possible scenarios. BI can also help create new ideas based on new patterns found from data mining. Operational decisions can be made more efficient using an analysis of past data.
A classification system can be created and modeled using the data of past instances to develop a good model of the domain. This model can help improve operational decisions in the future.
BI can help automate operations level decision-making and improve efficiency by making millions of microlevel operational decisions in a model-driven way. For example, a bank might want to make decisions about making financial loans in a more scientific way using data-based models. A decision-tree-based model could provide a consistently accurate loan decisions. Developing such decision tree models is one of the main applications of data mining techniques.
Effective BI has an evolutionary component, as business models evolve.
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When people and organizations act, new facts data are generated. Current business models can be tested against the new data, and it is possible that those models will not hold up well. In that case, decision models should be revised and new insights should be incorporated.
An unending process of generating fresh new insights in real time can help make better decisions, and thus can be a significant competitive advantage. Information can be provided about the current state of affairs with the capability to drill down into details, and also insights about emerging patterns which lead to projections into the future. BI tools include data warehousing, online analytical processing, social media analytics, reporting, dashboards, querying, and data mining.