Maximo is an IBM enterprise asset management for asset life-cycle and maintenance management. IBM Maximo® enterprise asset management solutions allow you to gain near real time visibility into asset usage, better govern assets, extend the useful life of capital equipment, improve return on assets and defer new purchases—while unifying processes for wide-ranging enterprising asset management functions across multiple sites.
Support enterprise asset management in key industries, including manufacturing, healthcare, life sciences, nuclear power, oil and gas, service providers, transportation and utilities.
Provide visibility and control over critical assets that affect compliance, risk and business performance.
Increase the useful life of physical assets with improved business processes for an increased return on assets and enhanced operational efficiency.
It has six major functions
Asset management – Achieve the control you need to more efficiently track and manage asset and location data throughout the asset lifecycle.
Work management – Manage both planned and unplanned work activities, from initial request through completion and recording of actuals.
Service management – Define service offerings, establish service level agreements (SLAs), more proactively monitor service level delivery and implement escalation procedures.
Contract management – Gain complete support for purchase, lease, rental, warranty, labor rate, software, master, blanket and user-defined contracts.
Inventory management – Know the details of asset-related inventory and its usage including what, when, where, how many and how valuable.
Procurement management – Support all phases of enterprise-wide procurement such as direct purchasing and inventory replenishment.
IBM MobileFirst Foundation, formerly known as IBM Worklight®, is a suite of software development products that allow developers to build and deliver mobile applications for the enterprise.
The MobileFirst Platform Foundation consists of:
MobileFirst Server – the middleware tier that provides a gateway between back-end systems and services and the mobile client applications.
MobileFirst API - both client and server-side APIs for developing and managing your enterprise mobile applications.
MobileFirst Studio - an optional all-inclusive development environment for developing enterprise apps on the MobileFirst platform. This is based on the Eclipse platform, and includes an integrated server, development environment, facilities to create and test all data adapters/services, a browser-based hybrid app simulator, and the ability to generate platform-specific applications for deployment.
MobileFirst Console – the console provides a dashboard and management portal for everything happening within your MobileFirst applications.
MobileFirst Application Center - a tool to make sharing mobile apps easier within an organization. Basically, it’s an app store for your enterprise.
BM talks about the MobileFirst Platform in two ways, based in its capabilities and also by its components. The capability areas are: Continuously Improve, Secure, Contextualize and Personalize, and Enrich with Data.
Continuously Improve - allows IT to manage application refresh cycles and collect in-app usage analytics. Secure - provides enterprise mobility management (EMM) capabilities. Contextualize and Personalize - allows developers to create mobile apps that are location- and context-aware. Enrich with Data - allows IT to join its mobile apps to internal and external data sources by connecting directly with IBM's Cloudant database as a service (DBaaS).
Caffe is a deep learning framework made with expression, speed, and modularity in mind
Expression: models and optimizations are defined as plaintext schemas instead of code.
Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data.
Modularity: new tasks and settings require flexibility and extension.
Openness: scientific and applied progress call for common code, reference models, and reproducibility.
Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project.
Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.
GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL provides a complete and understandable description of the data in your API, gives clients the power to ask for exactly what they need and nothing more, makes it easier to evolve APIs over time, and enables powerful developer tools.
GraphQL queries access not just the properties of one resource but also smoothly follow references between them. While typical REST APIs require loading from multiple URLs, GraphQL APIs get all the data your app needs in a single request. Apps using GraphQL can be quick even on slow mobile network connections.
GraphQL APIs are organized in terms of types and fields, not endpoints. Access the full capabilities of your data from a single endpoint. GraphQL uses types to ensure Apps only ask for what’s possible and provide clear and helpful errors. Apps can use types to avoid writing manual parsing code.
GraphQL creates a uniform API across your entire application without being limited by a specific storage engine. Write GraphQL APIs that leverage your existing data and code with GraphQL engines available in many languages. You provide functions for each field in the type system, and GraphQL calls them with optimal concurrency.
Apache Wicket, commonly referred to as Wicket, is a lightweight component-based web application framework for the Java programming language conceptually similar to JavaServer Faces and Tapestry. It was originally written by Jonathan Locke in April 2004. Version 1.0 was released in June 2005. It graduated into an Apache top-level project in June 2007.
Invented in 2004, Wicket is one of the few survivors of the Java serverside web framework wars of the mid 2000's. Wicket is an open source, component oriented, serverside, Java web application framework. With a history of over a decade, it is still going strong and has a solid future ahead. Learn why you should consider Wicket for your next web application.
Wicket uses plain XHTML for templating (which enforces a clear separation of presentation and business logic and allows templates to be edited with conventional WYSIWYG design tools). Each component is bound to a named element in the XHTML and becomes responsible for rendering that element in the final output. The page is simply the top-level containing component and is paired with exactly one XHTML template. Using a special tag, a group of individual components may be abstracted into a single component called a panel, which can then be reused whole in that page, other pages, or even other panels.
Each component is backed by its own model, which represents the state of the component. The framework does not have knowledge of how components interact with their models, which are treated as opaque objects automatically serialized and persisted between requests. More complex models, however, may be made detachable and provide hooks to arrange their own storage and restoration at the beginning and end of each request cycle. Wicket does not mandate any particular object-persistence or ORM layer, so applications often use some combination of Hibernate objects, EJBs or POJOs as models.
In Wicket, all server side state is automatically managed. You should never directly use an HttpSession object or similar wrapper to store state. Instead, state is associated with components. Each server-side page component holds a nested hierarchy of stateful components, where each component’s model is, in the end, a POJO (Plain Old Java Object)
Wicket is all about simplicity. There are no configuration files to learn in Wicket. Wicket is a simple class library with a consistent approach to component structure.
Apache Wicket is a simple and features rich component-based web framework, the real reusable components is the main selling point of this framework. However, due to the big different between component-based and MVC architecture, it makes Wicket hard to learn, especially for those classic MVC developers.
A Recommender System predicts the likelihood that a user would prefer an item. Based on previous user interaction with the data source that the system takes the information from (besides the data from other users, or historical trends), the system is capable of recommending an item to a user. Think about the fact that Amazon recommends you books that they think you could like; Amazon might be making effective use of a Recommender System behind the curtains. This simple definition, allows us to think in a diverse set of applications where Recommender Systems might be useful. Applications such as documents, movies, music, romantic partners, or who to follow on Twitter, are pervasive and widely known in the world of Information Retrieval.
Recommender systems are among the most popular applications of data science today. They are used to predict the "rating" or "preference" that a user would give to an item. Almost every major tech company has applied them in some form or the other: Amazon uses it to suggest products to customers, YouTube uses it to decide which video to play next on autoplay, and Facebook uses it to recommend pages to like and people to follow. What's more, for some companies -think Netflix and Spotify-, the business model and its success revolves around the potency of their recommendations. In fact, Netflix even offered a million dollars in 2009 to anyone who could improve its system by 10%.
Broadly, recommender systems can be classified into 3 types:
Simple recommenders: offer generalized recommendations to every user, based on movie popularity and/or genre. The basic idea behind this system is that movies that are more popular and critically acclaimed will have a higher probability of being liked by the average audience. IMDB Top 250 is an example of this system.
Content-based recommenders: suggest similar items based on a particular item. This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations. The general idea behind these recommender systems is that if a person liked a particular item, he or she will also like an item that is similar to it.
Collaborative filtering engines: these systems try to predict the rating or preference that a user would give an item-based on past ratings and preferences of other users. Collaborative filters do not require item metadata like its content-based counterparts.