Actionstep is a cloud-based legal practice management software for law firms and compliance-focused businesses. Actionstep is built to be a comprehensive practice management software with features for workflow automation as well as automatic document generation == History == Actionstep was created by Ted Jordan, CEO of Actionstep, in 2004. It was first used commercially in 2005 by a New Zealand construction franchise as well as a law firm. Actionstep soon expanded into central government and a wider range of small business users (mainly in New Zealand and Australia). After a few years the expanse of their legal client base prompted the company to add key legal specific features to the product with the aim of further expanding their legal market. Through Actionstep's tenure as a practice management software they have gradually expanded from their headquarters in New Zealand and offices located in the United Kingdom and the United States of America. In October 2020, private equity firm Serent Capital Partners purchased 84.25% stake in Actionstep. In April 2022, the company announced unlimited annual leave to its staff == Product == The premise of Actionstep is that it saves companies from having to purchase software tailored to their work flow and instead allows companies to modify the program without additional coding.{{Citation needed}} The founder and CEO Ted Jordan used cloud technology to allow the software to be continuously updated without the need to purchase or redesign new software. This theoretically allows businesses to remain current all the time and cut external I.T. costs.{{Citation needed}} Actionstep also integrates with software from other companies, such as Xero accounting, Microsoft Office & Office 365, Gmail, Google Drive, Dropbox, NetDocuments, QuickBooks, LawPay, BundleDocs, Box, HotDocs, Infotrack, GlobalX, PEXA, JOSEF and Zapier. Actionstep contains workflow automation features aimed at increasing office efficiency. These automated processes include automatic task assignment, information collection, document generation & automation, cataloguing, and matter generation. == Awards == Actionstep was named First International Best of SaaS Showplace Award Winner in 2009. Actionstep has also been a finalist in the ComputerWorld Excellence Awards (2007), and the Vero Excellence in Business Support (2010).
Control system
A control system manages, commands, directs, or regulates the behavior of other devices or systems using control loops. It can range from a single home heating controller using a thermostat controlling a domestic boiler to large industrial control systems which are used for controlling processes or machines. The control systems are designed via control engineering process. For continuously modulated control, a feedback controller is used to automatically control a process or operation. The control system compares the value or status of the process variable (PV) being controlled with the desired value or setpoint (SP), and applies the difference as a control signal to bring the process variable output of the plant to the same value as the setpoint. For sequential and combinational logic, software logic, such as in a programmable logic controller, is used. == Open-loop and closed-loop control == == Feedback control systems == == Logic control == Logic control systems for industrial and commercial machinery were historically implemented by interconnected electrical relays and cam timers using ladder logic. Today, most such systems are constructed with microcontrollers or more specialized programmable logic controllers (PLCs). The notation of ladder logic is still in use as a programming method for PLCs. Logic controllers may respond to switches and sensors and can cause the machinery to start and stop various operations through the use of actuators. Logic controllers are used to sequence mechanical operations in many applications. Examples include elevators, washing machines and other systems with interrelated operations. An automatic sequential control system may trigger a series of mechanical actuators in the correct sequence to perform a task. For example, various electric and pneumatic transducers may fold and glue a cardboard box, fill it with the product and then seal it in an automatic packaging machine. PLC software can be written in many different ways – ladder diagrams, SFC (sequential function charts) or statement lists. == On–off control == On–off control uses a feedback controller that switches abruptly between two states. A simple bi-metallic domestic thermostat can be described as an on-off controller. When the temperature in the room (PV) goes below the user setting (SP), the heater is switched on. Another example is a pressure switch on an air compressor. When the pressure (PV) drops below the setpoint (SP) the compressor is powered. Refrigerators and vacuum pumps contain similar mechanisms. Simple on–off control systems like these can be cheap and effective. == Linear control == == Fuzzy logic == Fuzzy logic is an attempt to apply the easy design of logic controllers to the control of complex continuously varying systems. Basically, a measurement in a fuzzy logic system can be partly true. The rules of the system are written in natural language and translated into fuzzy logic. For example, the design for a furnace would start with: "If the temperature is too high, reduce the fuel to the furnace. If the temperature is too low, increase the fuel to the furnace." Measurements from the real world (such as the temperature of a furnace) are fuzzified and logic is calculated arithmetic, as opposed to Boolean logic, and the outputs are de-fuzzified to control equipment. When a robust fuzzy design is reduced to a single, quick calculation, it begins to resemble a conventional feedback loop solution and it might appear that the fuzzy design was unnecessary. However, the fuzzy logic paradigm may provide scalability for large control systems where conventional methods become unwieldy or costly to derive. Fuzzy electronics is an electronic technology that uses fuzzy logic instead of the two-value logic more commonly used in digital electronics. == Physical implementation == The range of control system implementation is from compact controllers often with dedicated software for a particular machine or device, to distributed control systems for industrial process control for a large physical plant. Logic systems and feedback controllers are usually implemented with programmable logic controllers. The Broadly Reconfigurable and Expandable Automation Device (BREAD) is a recent framework that provides many open-source hardware devices which can be connected to create more complex data acquisition and control systems.
Azure Stream Analytics
Microsoft Azure Stream Analytics is a serverless scalable complex event processing engine by Microsoft that enables users to develop and run real-time analytics on multiple streams of data from sources such as devices, sensors, web sites, social media, and other applications. Users can set up alerts to detect anomalies, predict trends, trigger necessary workflows when certain conditions are observed, and make data available to other downstream applications and services for presentation, archiving, or further analysis. == Query Language == Users can author real-time analytics using a simple declarative SQL-like language with embedded support for temporal logic. Callouts to custom code with JavaScript user defined functions extend the streaming logic written in SQL. Callouts to Azure Machine Learning helps with predictive scoring on streaming data. == Scalability == Azure Stream Analytics is a serverless job service on Azure that eliminates the need for infrastructure, servers, virtual machines, or managed clusters. Users only pay for the processing used for the running jobs. == IoT applications == Azure Stream Analytics integrates with Azure IoT Hub to enable real-time analytics on data from IoT devices and applications. == Real-time Dashboards == Users can build real-time dashboards with Power BI for a live command and control view. Real-time dashboards help transform live data into actionable and insightful visuals. == Data Input Sources == Stream Analytics supports three different types of input sources - Azure Event Hubs, Azure IoT Hubs, and Azure Blob Storage. Additionally, stream analytics supports Azure Blob storage as the input reference data to help augment fast moving event data streams with static data. Stream analytics supports a wide variety of output targets. Support for Power BI allows for real-time dashboarding. Event Hub, Service bus topics and queues help trigger downstream workflows. Support for Azure Table Storage, Azure SQL Databases, Azure SQL Data Warehouse, Azure SQL, Document DB, Azure Data Lake Store enable a variety of downstream analysis and archiving capabilities.
Umple
Umple is a language for both object-oriented programming and modelling with class diagrams and state diagrams. The name Umple is a portmanteau of "UML", "ample" and "Simple", indicating that it is designed to provide ample features to extend programming languages with UML capabilities. == History and philosophy == The design of Umple started in 2008 at the University of Ottawa. Umple was open-sourced and its development was moved to Google Code in early 2011 and to GitHub in 2015. Umple was developed, in part, to address certain problems observed in the modelling community. Most specifically, it was designed to bring modelling and programming into alignment, It was intended to help overcome inhibitions against modelling common in the programmer community. It was also intended to reduce some of the difficulties of model-driven development that arise from the need to use large, expensive or incomplete tools. One design objective is to enable programmers to model in a way they see as natural, by adding modelling constructs to programming languages. == Features and capabilities == Umple can be used to represent in a textual manner many UML modelling entities found in class diagrams and state diagrams. Umple can generate code for these in various programming languages. Currently Umple fully supports Java, C++ and PHP as target programming languages and has functional, but somewhat incomplete support for Ruby. Umple also incorporates various features not related to UML, such as the singleton pattern, keys, immutability, mixins and aspect-oriented code injection. The class diagram notations Umple supports includes classes, interfaces, attributes, associations, generalizations and operations. The code Umple generates for attributes include code in the constructor, 'get' methods and 'set' methods. The generated code differs considerably depending on whether the attribute has properties such as immutability, has a default value, or is part of a key. Umple generates many methods for manipulating, querying and navigating associations. It supports all combinations of UML multiplicity and enforces referential integrity. Umple supports the vast majority of UML state machine notation, including arbitrarily deep nested states, concurrent regions, actions on entry, exit and transition, plus long-lasting activities while in a state. A state machine is treated as an enumerated attribute where the value is controlled by events. Events encoded in the state machine can be methods written by the user, or else generated by the Umple compiler. Events are triggered by calling the method. An event can trigger transitions (subject to guards) in several different state machines. Since a program can be entirely written around one or more state machines, Umple enables automata-based programming. The bodies of methods are written in one of the target programming languages. The same is true for other imperative code such as state machine actions and guards, and code to be injected in an aspect-oriented manner. Such code can be injected before many of the methods in the code Umple generates, for example before or after setting or getting attributes and associations. The Umple notation for UML constructs can be embedded in any of its supported target programming languages. When this is done, Umple can be seen as a pre-processor: The Umple compiler expands the UML constructs into code of the target language. Code in a target language can be passed to the Umple compiler directly; if no Umple-specific notation is found, then the target-language code is emitted unchanged by the Umple compiler. Umple, combined with one of its target languages for imperative code, can be seen and used as a complete programming language. Umple plus Java can therefore be seen as an extension of Java. Alternatively, if imperative code and Umple-specific concepts are left out, Umple can be seen as a way of expressing a large subset of UML in a purely textual manner. Code in one of the supported programming languages can be added in the same manner as UML envisions adding action language code. == License == Umple is licensed under an MIT-style license. == Examples == Here is the classic Hello world program written in Umple (extending Java): This example looks just like Java, because Umple extends other programming languages. With the program saved in a file named HelloWorld.ump, it can be compiled from the command line: $ java -jar umple.jar HelloWorld.ump To run it: $ java HelloWorld The following is a fully executable example showing embedded Java methods and declaration of an association. The following example describes a state machine called status, with states Open, Closing, Closed, Opening and HalfOpen, and with various events that cause transitions from one state to another. class GarageDoor { status { Open { buttonOrObstacle -> Closing; } Closing { buttonOrObstacle -> Opening; reachBottom -> Closed; } Closed { buttonOrObstacle -> Opening; } Opening { buttonOrObstacle -> HalfOpen; reachTop -> Open; } HalfOpen { buttonOrObstacle -> Opening; } } } == Umple use in practice == The first version of the Umple compiler was written in Java, Antlr and Jet (Java Emitter Templates), but in a bootstrapping process, the Java code was converted to Umple following a technique called Umplification. The Antlr and Jet were also later converted to native Umple. Umple is therefore now written entirely in itself, in other words it is self-hosted and serves as its own largest test case. Umple and UmpleOnline have been used in the classroom by several instructors to teach UML and modelling. In one study it was found to help speed up the process of teaching UML, and was also found to improve the grades of students. == Tools == Umple is available as a Jar file so it can be run from the command line, and as an Eclipse plugin. There is also an online tool for Umple called UmpleOnline , which allows a developer to create an Umple system by drawing a UML class diagram, editing Umple code or both. Umple models created with UmpleOnline are stored in the cloud. Currently UmpleOnline only supports Umple programs consisting of a single input file. In addition to code, Umple's tools can generate a variety of other types of output, including user interfaces based on the Umple model.
Oracle Cloud Platform
Oracle Cloud Platform refers to a Platform as a Service (PaaS) offerings by Oracle Corporation as part of Oracle Cloud Infrastructure. These offerings are used to build, deploy, integrate and extend applications in the cloud. The offerings support a variety of programming languages, databases, tools and frameworks including Oracle-specific, open source and third-party software and systems. == Deployment models == Oracle Cloud Platform offers public, private and hybrid cloud deployment models. == Architecture == Oracle Cloud Platform provides both Infrastructure as a Service (IaaS) and Platform as a Service (PaaS). The infrastructure is offered through a global network of Oracle managed data centers. Oracle deploys their cloud in Regions. Inside each Region are at least three fault-independent Availability Domains. Each of these Availability Domains contains an independent data center with power, thermal and network isolation. Oracle Cloud is generally available in North America, EMEA, APAC and Japan with announced South America and US Govt. regions coming soon.
Cryptographic module
A cryptographic module is a component of a computer system that securely implements cryptographic algorithms, typically with some element of tamper resistance. NIST defines a cryptographic module as "The set of hardware, software, and/or firmware that implements security functions (including cryptographic algorithms), holds plaintext keys and uses them for performing cryptographic operations, and is contained within a cryptographic module boundary." Hardware security modules, including secure cryptoprocessors, are one way of implementing cryptographic modules. Standards for cryptographic modules include FIPS 140-3 and ISO/IEC 19790.
Cloud computing
Cloud computing is defined by the International Organization for Standardization (ISO) as "a paradigm for enabling network access to a scalable and elastic pool of shareable physical or virtual resources with self-service provisioning and administration on demand". It is commonly referred to as "the cloud". == Characteristics == In 2011, the National Institute of Standards and Technology (NIST) identified five "essential characteristics" for cloud systems. Below are the exact definitions according to NIST: On-demand self-service: "A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider." Broad network access: "Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, tablets, laptops, and workstations)." Resource pooling: " The provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand." Rapid elasticity: "Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear unlimited and can be appropriated in any quantity at any time." Measured service: "Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. By 2023, the International Organization for Standardization (ISO) had expanded and refined the list. == History == The history of cloud computing extends to the 1960s, with the initial concepts of time-sharing becoming popularized via remote job entry (RJE). The "data center" model, where users submitted jobs to operators to run on mainframes, was predominantly used during this era. This period saw broad experimentation with making large-scale computing power more accessible through time-sharing, while optimizing infrastructure, platforms, and applications to improve efficiency for end users. The "cloud" metaphor for virtualized services dates to 1994, when it was used by General Magic for the universe of "places" that mobile agents in the Telescript environment could "go". The metaphor is credited to David Hoffman, a General Magic communications specialist, based on its long-standing use in networking and telecom. The expression cloud computing became more widely known in 1996 when Compaq Computer Corporation drew up a business plan for future computing and the Internet. The company's ambition was to supercharge sales with "cloud computing-enabled applications". The business plan foresaw that online consumer file storage would likely be commercially successful. As a result, Compaq decided to sell server hardware to internet service providers. In the 2000s, the application of cloud computing began to take shape with the establishment of Amazon Web Services (AWS) in 2002, which allowed developers to build applications independently. In 2006 Amazon Simple Storage Service, known as Amazon S3, and the Amazon Elastic Compute Cloud (EC2) were released. In 2008 NASA's development of the first open-source software for deploying private and hybrid clouds. The following decade saw the launch of various cloud services. In 2010, Microsoft launched Microsoft Azure, and Rackspace Hosting and NASA initiated an open-source cloud-software project, OpenStack. IBM introduced the IBM SmartCloud framework in 2011, and Oracle announced the Oracle Cloud in 2012. In December 2019, Amazon launched AWS Outposts, a service that extends AWS infrastructure, services, APIs, and tools to customer data centers, co-location spaces, or on-premises facilities. == Value proposition == Cloud computing can shorten time to market by offering pre-configured tools, scalable resources, and managed services, allowing users to focus on core business value rather than maintaining infrastructure. Cloud platforms can enable organizations and individuals to reduce upfront capital expenditures on physical infrastructure by shifting to an operational expenditure model, where costs scale with usage. Cloud platforms also offer managed services and tools, such as artificial intelligence, data analytics, and machine learning, which might otherwise require significant in-house expertise and infrastructure investment. While cloud computing can offer cost advantages through effective resource optimization, organizations often face challenges such as unused resources, inefficient configurations, and hidden costs without proper oversight and governance. Many cloud platforms provide cost management tools, such as AWS Cost Explorer and Azure Cost Management, and frameworks like FinOps have emerged to standardize financial operations in the cloud. Cloud computing also facilitates collaboration, remote work, and global service delivery by enabling secure access to data and applications from any location with an internet connection. Cloud providers offer various redundancy options for core services, such as managed storage and managed databases, though redundancy configurations often vary by service tier. Advanced redundancy strategies, such as cross-region replication or failover systems, typically require explicit configuration and may incur additional costs or licensing fees. Cloud environments operate under a shared responsibility model, where providers are typically responsible for infrastructure security, physical hardware, and software updates, while customers are accountable for data encryption, identity and access management (IAM), and application-level security. These responsibilities vary depending on the cloud service model—Infrastructure as a Service (IaaS), Platform as a Service (PaaS), or Software as a Service (SaaS)—with customers typically having more control and responsibility in IaaS environments and progressively less in PaaS and SaaS models, often trading control for convenience and managed services. == Adoption and suitability == The decision to adopt cloud computing or maintain on-premises infrastructure depends on factors such as scalability, cost structure, latency requirements, regulatory constraints, and infrastructure customization. Organizations with variable or unpredictable workloads, limited capital for upfront investments, or a focus on rapid scalability benefit from cloud adoption. Startups, SaaS companies, and e-commerce platforms often prefer the pay-as-you-go operational expenditure (OpEx) model of cloud infrastructure. Additionally, companies prioritizing global accessibility, remote workforce enablement, disaster recovery, and leveraging advanced services such as AI/ML and analytics are well-suited for the cloud. In recent years, some cloud providers have started offering specialized services for high-performance computing and low-latency applications, addressing some use cases previously exclusive to on-premises setups. On the other hand, organizations with strict regulatory requirements, highly predictable workloads, or reliance on deeply integrated legacy systems may find cloud infrastructure less suitable. Businesses in industries like defense, government, or those handling highly sensitive data often favor on-premises setups for greater control and data sovereignty. Additionally, companies with ultra-low latency requirements, such as high-frequency trading (HFT) firms, rely on custom hardware (e.g., FPGAs) and physical proximity to exchanges, which most cloud providers cannot fully replicate despite recent advancements. Similarly, tech giants like Google, Meta, and Amazon build their own data centers due to economies of scale, predictable workloads, and the ability to customize hardware and network infrastructure for optimal efficiency. However, these companies also use cloud services selectively for certain workloads and applications where it aligns with their operational needs. In practice, many organizations are increasingly adopting hybrid cloud architectures, combining on-premises infrastructure with cloud services. This approach allows businesses to balance scalability, cost-effectiveness, and control, offering the benefits of both deployment models while mitigating their respective limitations. == Challenges and limitations == One of the primary challenges of cloud computing, compared with traditional on-premises systems, is maintaining data security and privacy. Cloud users entrust their sensitive data to third-party providers, who may not have adequate measures to protect it from unau