The term big data generally describes an amount of data that is too big, too complex, too fast moving or too weakly structured to be analyzed with conventional means of data processing. Being an umbrella term, the meaning of ‘big data’ is subject to constant change and not only stands for a high amount of data, but also for using, collecting and (commercially) exploiting it. At the same time, the term , unlike any other concept, describes the new era of digital communication and technology, which, technologically as well as socially, will lead to immense, global changes in society.
The collected data, which is labeled ‘big data’ can derive from very different sources and come up in various fields of economy, in businesses, but also in public and private life. Examples can be recordings of monitoring systems, the use of banking or loyalty cards, any type of electronic communication and the use of electronic devices, such as smartphones or smartwatches, social media, cross-linked technology in homes and vehicles, etc.
The question of how to deal with big data in processing and analyzing is one of the biggest challenges in current software development, as ordinary database systems are often incapable of working with it.
You are looking for an individual solution to exploit the potential of big data? IDA is the right partner for you!
In Complex Event Processing (CEP), we have an innovative technology which will cause profound effects. With the help of CEP, it is now possible to analyze high amounts of data from different sources in real-time.
Applications have to be capable of constantly analyzing a high amount of incoming data, taking into account subject-specific and temporal criteria. Database-assisted applications have not been designed to fulfil this task.
CEP provides help with the real-time processing and analysis of process-oriented information (so called event streams) in order to be able to discover and predict correlation and dependence (event patterns) between process events. The results are called complex events. CEP does not rely on traditional database technology, but on innovative algorithms, which are running in the central memory and process incoming elements incrementally. The end user formulates the necessary steps with ‘continuous’ queries.
We are capable of gaining the right information from data.
According to a definition given by Plattform Industrie 4.0, the term Industry 4.0 describes a new kind of organization and management taking into account the entire value-added chain over the service life of products. This comprises all the different steps: idea, order, development and production, shipping to the consumer and recycling and includes all the services involved. According to Plattform Industrie 4.0, this approach leads to being able to fulfill individual customer wishes sustainably.
The foundation of this definition is a comprehensive management based on the permanent availability of all relevant information in real-time, the interconnection of all instances involved in the value added, as well as the capability of deriving the ideal value creation process from the data at any time. The connection between humans, objects and systems makes it possible to build adaptable, real-time optimized and self-organizing, cross-company networks, which can be optimized according to different criteria, such as e.g. costs, availability or consumption of resources. This vision of the setup and functioning of Industry 4.0 systems represents an advancement of the cross-linkage of intelligent objects, which can also be found in the concept of the Internet of Things.
The trend towards Networked Worlds and Industry 4.0 is being pursued mainly due to the rapid development in the number of cross-linkable devices.
A catchphrase which is currently essential in every sector. Even if there is no universal definition – the Internet of Things essentially describes the communication of intelligent devices with one another.
The devices involved usually communicate with one another controlled by microprocessors, in order to collect status information about themselves and their immediate surroundings and send them to further, cross-linked objects (e.g. computers, sensors and machines). Thus, it is possible to synchronically analyze and process the collected data and lay the foundation for an advanced technology adapted to customer needs. This leads to varied added value for the customer: e-health (e.g. fitness trackers, intelligent pacemakers), smart home (e.g. intelligent lighting, central control and notification via app), and smart cars (self-driving cars, assistance systems).
In Industry 4.0, production is also profiting and experiencing an enormous boost. Whereas in the past, machines used to work dully on their own, today whole factory halls are cross-linked. This does not only facilitate controlling and monitoring entire production lines. It is also possible to switch quickly between different products or even to individualized goods, such as smaller series or individual items. Warehouses are becoming smaller due to just-in-time production. Smart production supports the entire supply chain management, including the choice of suppliers.
Predictive Maintenance describes foresighted maintenance of a production process with the help of intelligent data analytics. Machines, products and components of the systems involved in the process are cross-linked – thus, you make use of the Internet of Things.
Thanks to the use of sensors, this cross-linkage serves to collect status data of machines and combine them with information form third-party systems (ERP-, CRM systems). The goal is to detect suspicious patterns indicating malfunction early enough to initiate the appropriate preventive measures. Impending errors, breakdowns and resulting downtimes in the production process can thus be detected in time and the appropriate maintenance can be initiated. Predictive Maintenance is an important component in a 4.0 environment and is a further development of established maintenance strategies.
With the right choice of systems, the generation and preparation of the correct data, as well as an appropriate mix of industry expertise and data analytics, the success of a Predictive Maintenance solution is guaranteed. IDA can support you in the development of the right solution for your business.
Every day, business systems generate huge amounts of data, which, however, is only in part processed and analyzed. In the field of security, it is processed with software relating to the topic of SIEM (Security Information and Event Management).
Conventional SIEM solutions are usually used for the analysis of log files. For this purpose, rules reacting to known events are defined. However, by implication, this means that they cannot react early enough to undefined anomalies and events. SIEM systems are normally restricted to infrastructural events, and only provide limited support in making decisions. With our security solutions, we tackle the weaknesses of conventional SIEM solutions. IDA provides an innovative security system for conventional server environments and heterogeneous visualization environments that compensates for the disadvantages of current SIEM solutions. By means of abstraction, correlation, aggregation and pattern detection of company-wide data, our solutions can implement an automatized behavioral and statistical analysis with multidimensional analysis functions. By monitoring all relevant events and anomalies, the users and systems of the business are protected. By combining statistical methods and heuristics, we enable businesses to “learn” “normal” behavior in any desired combination of attributes of users, hosts, applications or devices automatically.
Constantly monitoring the IT environment with a Smart Monitoring solution allows an early detection of critical statuses in your server systems, services and their applications.
The operation of complex IT infrastructures requires the employment of professional equipment and experts to supervise the systems. A malfunction of business-critical applications inevitably leads to economic losses. With the graphical user interface, events can be pursued, searched and filtered.
Notifications can be transmitted in real-time via SMPT, SNMP, Syslog or HTTP POST.
Admins have the possibility to filter the results according to event, date or IP address. Only data matching the admin’s field of duty is displayed. Third-party companies can also send messages to SIEM tools.
Currently, a lot of explanatory work is to be done in the field of process optimization, as the term ‘big data’ in particular is sometimes used in a non-transparent and incorrect way. However, it is a fact that process-mining solutions that can process huge amounts of data in real-time and additionally, considerably simplify and accelerate process optimization in connection with Business Intelligence (BI) are already available.
By continually analyzing and structuring semi-structured process data from IT-Systems, companies gain a completely new basis of decision-making and can satisfy different needs of information – starting with compliance aspects through to very concrete questions such as “How much discount do I lose with supplier XY?” In addition to aggregated and cleansed databases, which classical BI solutions exclusively use for analytics, companies can access actually running process data, analyze and adjust processes and monitor the success in real-time.
Enabling technologies such as Automated Business Process Discovery and Process Business Intelligence are already functioning big data approaches which are being used in companies. In the near future, these approaches – to different extents – will become standard solutions in companies, regardless of sector and size, as there is an inherent potential of optimization in IT data, which can be exploited with the help of process data analytics.
Market-ready Industry 4.0 applications, such as Predictive Maintenance have strongly directed the focus of the manufacturing industry towards production IT: Machines and means of production which independently exchange information, which operate each other and maintain themselves autonomously are making factories increasingly smart. IT and production mingle, automatize production processes and change business relations between employees, customers and suppliers. Producing companies that want to benefit from the blessings of digitalization should not only connect their production IT. In order to make the right information available in the right place and at the right time, they also need to ensure a secure cooperation along the entire value-added chain. In industry, there is hardly anything as disadvantageous as a poor quality of products and an interruption of production processes. Both lead to losses in production and revenue. Since IT entered the field of production, more and more companies have been using intelligent software solutions in order to minimize production- and process-related difficulties. At the same time, with Industry 4.0, users have been aiming to improve the capacity utilization in factories and fulfill individual customer wishes more quickly.
Thus, the optimization of production processes also has an effect on the way of working of everyone involved: the production worker, who cooperates with different departments, the supplier, who provides the right material in the quality required, the shipper, who transports the goods on schedule, and, of course, on the customer, whose individual wishes are taken into account throughout the whole production process.