Timeboxing Timeboxing is a somewhat overlooked technique in project management. It has been around for decades and seems to go through periods of being fashionable, then unfashionable. This white paper on timeboxing explains what it is, when to use timeboxing, and how to run a timebox. Number of ratings - 16 Managing Uncertainty in Project Planning Uncertainty in project planning is widely recognized in many industries and a wide variety of different tools exists to help optimise the planning process and minimize the associated risks.
Forum As ofDeepDive project is in maintenance mode and no longer under active development. What does DeepDive do?
DeepDive is a system to extract value from dark data. Like dark matter, dark data is the great mass of data buried in text, tables, figures, and images, which lacks structure and so is essentially unprocessable by existing software.
DeepDive helps bring dark data to light by creating structured data SQL tables from unstructured information text documents and integrating such data with an existing structured database.
DeepDive is used to extract sophisticated relationships between entities and make inferences about facts involving those entities.
DeepDive helps one process a wide variety of dark data and put the results into a database. With the data in a database, one can use a variety of standard tools that consume structured data; e. DeepDive is a new type of data management system that enables one to tackle extraction, integration, and prediction problems in a single system, which allows users to rapidly construct sophisticated end-to-end data pipelines, such as dark data BI Business Intelligence systems.
By allowing users to build their system end-to-end, DeepDive allows users to focus on the portion of their system that most directly improves the quality of their application. By contrast, previous pipeline-based systems require developers to build extractors, integration code, and other components—without Construction project management research papers clear idea of how their changes improve the quality of their data product.
This simple insight is the key to how DeepDive systems produce higher quality data in less time. DeepDive-based systems are used by users without machine learning expertise in a number of domains from paleobiology to genomics to human trafficking; see our showcase for examples.
DeepDive is a trained system that uses machine learning to cope with various forms of noise and imprecision. DeepDive is designed to make it easy for users to train the system through low-level feedback via the Mindtagger interface and rich, structured domain knowledge via rules.
DeepDive wants to enable experts who do not have machine learning expertise. One of DeepDive's key technical innovations is the ability to solve statistical inference problems at massive scale.
DeepDive differs from traditional systems in several ways: DeepDive asks the developer to think about features—not algorithms.
In contrast, other machine learning systems require the developer think about which clustering algorithm, which classification algorithm, etc. In DeepDive's joint inference based approach, the user only specifies the necessary signals or features.
DeepDive systems can achieve high quality: PaleoDeepDive has higher quality than human volunteers in extracting complex knowledge in scientific domains and winning performance in entity relation extraction competitions.
DeepDive is aware that data is often noisy and imprecise: Taking such imprecision into account, DeepDive computes calibrated probabilities for every assertion it makes. For example, if DeepDive produces a fact with probability 0. DeepDive is able to use large amounts of data from a variety of sources.
Applications built using DeepDive have extracted data from millions of documents, web pages, PDFs, tables, and figures.
DeepDive allows developers to use their knowledge of a given domain to improve the quality of the results by writing simple rules that inform the inference learning process. DeepDive can also take into account user feedback on the correctness of the predictions to improve the predictions. DeepDive is able to use the data to learn "distantly".
In contrast, most machine learning systems require tedious training for each prediction. In fact, many DeepDive applications, especially in early stages, need no traditional training data at all! DeepDive's secret is a scalable, high-performance inference and learning engine.
For the past few years, we have been working to make the underlying algorithms run as fast as possible. The techniques pioneered in this project are part of commercial and open source tools including MADlibImpalaa product from Oracleand low-level techniques, such as Hogwild!
They have also been included in Microsoft's Adam and other major web companies. For more details, check out our papers. What is DeepDive used for? Examples of DeepDive applications are described in our showcase page.Learn how the strategic and committed use of project, program and portfolio management supports greater success for organizations.
Papers. THP collaborates with leading experts to produce evidence-based policy proposals that foster prosperity through broad-based, sustainable economic growth. Project Management Issues in Construction Sites Environment - Abstract The main purpose of this research is to investigate project management issues in construction sites environment, to recognize which issues are more vital for overall success of any construction project or vice versa and to suggest recommendations for improvement of .
This research explores the impact of real-time events on managers during decision making processes for project portfolio management, using a purpose built simuation. The simulation devleopment was informed by the Cynefin framework. DLR Group broke ground on a seven-story mass wood construction project in Atlanta.
It encompasses , square feet near the cities 24/7 Atlantic Station area.
What does DeepDive do? DeepDive is a system to extract value from dark heartoftexashop.com dark matter, dark data is the great mass of data buried in text, tables, figures, and images, which lacks structure and so is essentially unprocessable by existing software.