An event often only requires a simple response rather than complex processing. However, events can originate anywhere, and the frequency of events can range from zero to tens of thousands per second. Furthermore, being cloud-based, serverless computing is less likely to fail if some cloud resources are lost. Pay-per-use costs associated with serverless have both advantages and disadvantages. The advantage is that you only pay for what you use. The disadvantage is that if there are a lot of events, your costs can rise dramatically.
A common framework under which the various studies on terminology processing can be viewed is to consider not only the texts from which the terminological. Application-Driven Terminology Engineering. Special issue of Terminology ( ). Editors Application-oriented terminography in financial forensics.
Consequently, serverless becomes expensive if you underestimate the number of events. If cost is an important consideration, there is the alternative of using container-based event processing instead of serverless, where the event-handling functions are placed in containers on a single host or VM. This creates a ceiling cost on the single host. The future of digital business lies in an expanding, intelligent, digital mesh that will be composed of event-driven applications, IoT, cloud computing, blockchain, in-memory data management and AI.
It goes without saying that as our digital world expands, so does the importance of event-driven computing, which will allow business events to be detected faster and analysed in greater detail. Find Out What We Do. Your email address will not be published. Save my name, email, and website in this browser for the next time I comment. In more than pages, the Handbook brings together contributions from approximately 50 expert authorities in the field.
The Handbook covers a broad range of topics integrated from an international perspective and treats such fundamental issues as: practical methods of terminology management; creation and use of terminological tools terminology databases, on-line dictionaries, etc. The high level of expertise provided by the contributors, combined with the wide range of perspectives they represent, results in a thorough coverage of all facets of a burgeoning field.
The lay-out of the Handbook is specially designed for quick and for cross reference, with hypertext and an extensive index. You are commenting using your WordPress. You are commenting using your Google account. You are commenting using your Twitter account.
You are commenting using your Facebook account. Distributed File System — systems that offer simplified, highly available access to storing, analysing and processing data Document Store Databases — a document-oriented database that is especially designed to store, manage and retrieve documents, also known as semi structured data.
Exploratory analysis — finding patterns within data without standard procedures or methods. It is a means of discovering the data and to find the data sets main characteristics. Exabytes — approximately petabytes or 1 billion gigabytes.
Stahl, M. The activity of locating duplicates or fragments of code with a high degree of similarity and redundancy. The aim of education in systems engineering is to formalize various approaches simply and in doing so, identify new methods and research opportunities similar to that which occurs in other fields of engineering. In these patterns, the Buddhist video is the input of the pp. As a result of growing involvement from systems engineers outside of the U.
Today we create one Exabyte of new information globally on a daily basis. Extract, Transform and Load ETL — a process in a database and data warehousing meaning extracting the data from various sources, transforming it to fit operational needs and loading it into the database. Failover — switching automatically to a different server or node should one fail Fault-tolerant design — a system designed to continue working even if certain parts fail. Gamification — using game elements in a non game context; very useful to create data therefore coined as the friendly scout of big data Graph Databases — they use graph structures a finite set of ordered pairs or certain entities , with edges, properties and nodes for data storage.
It provides index-free adjacency, meaning that every element is directly linked to its neighbour element. Grid computing — connecting different computer systems from various location, often via the cloud, to reach a common goal.
Hadoop — an open-source framework that is built to enable the process and storage of big data across a distributed file system HBase — an open source, non-relational, distributed database running in conjunction with Hadoop HDFS — Hadoop Distributed File System; a distributed file system designed to run on commodity hardware High-Performance-Computing HPC — using supercomputers to solve highly complex and advanced computing problems. In-memory — a database management system stores data on the main memory instead of the disk, resulting is very fast processing, storing and loading of the data Internet of Things — ordinary devices that are connected to the internet at any time any where via sensors.
Juridical data compliance — relevant when you use cloud solutions and where the data is stored in a different country or continent. Be aware that data stored in a different country has to oblige to the law in that country. KeyValue Databases — they store data with a primary key, a uniquely identifiable record, which makes easy and fast to look up. The data stored in a KeyValue is normally some kind of primitive of the programming language. Latency — a measure of time delayed in a system Legacy system — an old system, technology or computer system that is not supported any more Load balancing — distributing workload across multiple computers or servers in order to achieve optimal results and utilization of the system Location data — GPS data describing a geographical location Log file — a file automatically created by a computer to record events that occur while operational.
Machine2Machine data — two or more machines that are communicating with each other Machine data — data created by machines via sensors or algorithms Machine learning — part of artificial intelligence where machines learn from what they are doing and become better over time MapReduce — a software framework for processing vast amounts of data Massively Parallel Processing MPP — using many different processors or computers to perform certain computational tasks at the same time Metadata — data about data; gives information about what the data is about.
MultiValue Databases — they are a type of NoSQL and multidimensional databases that understand 3 dimensional data directly. Natural Language Processing — a field of computer science involved with interactions between computers and human languages Network analysis — viewing relationships among the nodes in terms of the network or graph theory, meaning analysing connections between nodes in a network and the strength of the ties.
It is more consistent and can achieve higher availability and horizontal scaling. Object Databases — they store data in the form of objects, as used by object-oriented programming. They are different from relational or graph databases and most of them offer a query language that allows object to be found with a declarative programming approach. Object-based Image Analysis — analysing digital images can be performed with data from individual pixels, whereas object-based image analysis uses data from a selection of related pixels, called objects or image objects.
Operational Databases — they carry out regular operations of an organisation and are generally very important to a business.
They generally use online transaction processing that allows them to enter, collect and retrieve specific information about the company. Optimization analysis - the process of optimization during the design cycle of products done by algorithms. It allows companies to virtually design many different variations of a product and to test that product against pre-set variables. Ontology — ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts Outlier detection — an outlier is an object that deviates significantly from the general average within a dataset or a combination of data.
It is numerically distant from the rest of the data and therefore, the outlier indicates that something is going on and generally therefore requires additional analysis. Pattern Recognition — identifying patterns in data via algorithms to make predictions of new data coming from the same source. Petabytes - approximately terabytes or 1 million gigabytes.
The CERN Large Hydron Collider generates approximately 1 petabyte per second Platform-as-a-Service — a services providing all the necessary infrastructure for cloud computing solutions Predictive analysis — the most valuable analysis within big data as they help predict what someone is likely to buy, visit, do or how someone will behave in the near future. It uses a variety of different data sets such as historical, transactional, social or customer profile data to identify risks and opportunities. Quantified Self — a movement to use application to track ones every move during the day in order to gain a better understanding about ones behaviour Query — asking for information to answer a certain question.