Intelligent Search

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Your Challenges

With storage cost dropping and even large SSD devices coming into the price range for private consumers, it has become easy to collect and accumulate information - and never throw it away again. Capacities in the range of Petabytes have become a new standard for large organizations, not simplifying the task of employees to quickly locate information or to investigate what is available on a certain topic. Digital transformation processes digitize legacy information into electronic form and further contribute to volume and complexity.

Often, relying on colleagues and partners is the only way to reliably locate pieces of information needed. However, as in agile organizations the roles and functions of employees may change unexpectedly, and as skills evolve over time without being formally described, alone the task of identifying which colleagues may be suitable to ask has become a major challenge.

Plain text search through large collections of documents is neither efficient, nor is it guaranteed to find what one is looking for. Either there are far too many results or none. Tagging is an approach to attach semantic information to documents, however, this practice is often neglected and nobody wants to re-tag documents on a large scale because of new developments. As an example: documents describing certain explosives may have to be re-tagged with "safety" because of the invention of the airbag requiring these fast-acting, gas-producing explosives.

Consequently, what is called for is a more automated approach to reach a higher semantic coverage of information in an organization. Intelligent search is a key requirement needed in any knowledge-intensive enterprise to rapidly and accurately explore and find information in today's large piles of data from the Internet, corporate file servers and applications.

Our Solutions

We create solutions to collect, process and index pieces of any type of information on a potentially large scale. Processing includes semantic extraction and enrichment, i.e., steps necessary to automatically derive meta-data, categorizations and relationships - according to rules, linguistic heuristics, or machine learning components. This will help later with search and exploration processes but also to facilitates active alerting for users who want to be informed if there is new information matching their profiles of interest. The semantic extraction of features enables a point-and-click metaphor to easily navigate through even large unknown bodies of information. Mobile devices with their limited keyboard input capabilities greatly benefit from this approach.

We create powerful enterprise search platforms to index information from a large variety of source in one single platform and to provide a uniform search experience across all data sources. Security restrictions of source systems will be honored. We provide linguistic components for semantic enrichment and extraction of features. Where necessary, other media analytics (e.g., audio, image or video analytics) may be integrated as well. Enterprise search may also have a tight integration with Big Data solutions.
It is crucial for the success of e-commerce and information distribution sites to provide adequate search functions, but also means of exploring the products, services or information offered. We create powerful search experiences with auto-completion, recommendations and social ranking for your online shops, your intranet or your websites.
Mobile devices are characterized by cumbersome text input methods. If possible, mobile applications should offer point-and-click semantics to target smartphones and tablets. Faceted search with navigators that are determined ad-hoc and dynamically provide this even on large-scale information repositories with many meta-data fields and a mix of structured and unstructured values.
Structured data (e.g., from databases and data warehouses) and unstructured text (e.g., from websites, file systems or e-mail) are not the only media relevant for search. We can also include images, audio and video to extract meta-data, categorize and enrich, and to provide an integrated search capability over all media indexed. Visual search takes images as "search terms" and looks for matching items. Search in audio tracks may employ emotion detection, keyphrase spotting or even speech-to-text components to locate the information desired. Video search can identify moving objects or automatically provide synopses.
Many applications are retrieval-oriented, i.e., a small number of authoring users creates and manages contents of a database that is queried by many. Typical examples include telephone directories, information registers, product and service catalogues, and IT landscape documentation. Such applications can be replaced by using a small database for the authoring process but relying on a search platform for the quick retrieval according to arbitrary criteria. Linguistic features will add tolerance and more powerful search options. Search-based applications are a viable, cost-effective alternative to scaling classical database-driven services.