A Novel Framework for Identifying Customers’ Unmet Needs on Online Social Media Using Context Tree

Customer needs and user contexts play an important role in generating ideas for new products or new functions. This study proposes a novel framework for identifying customers’ unmet needs on online social media using the Context Tree through the Hierarchical Search of Concept Spaces (HSCS) algorithm. The Context Tree represents the hierarchical structure of nodes associated with related keywords and corresponding concept spaces. Unlike other methods, the Context Tree focuses on finding the unmet needs of customers from online social media. The proposed framework is applied to extract customer needs for home appliances. Identified customer needs are used to make user scenarios, which are used to develop new functions of home appliances.

Algorithmic Integration of Budgets into Strategic Enterprise Management

The author offers new approaches to dealing with the strategic and operational management of the company. The model of guaranteed algorithmic impact of strategic requirements (recommendations) expressed in specific indicators on monthly budgeting process is offered in the article. The organic interaction of these control types will allow management to implement model developments systematically and consistently in the enterprise. Embedding monthly budgets in strategic management involves the creation of intellectual information system, the core of which would be the hierarchical knowledge base that provides algorithmic communication between the strategy and budget. In addition, the database of formulas must be created which are designed for monthly calculations of indicators that are defined by the strategic goal. The provided in the article technology of fuzzy hierarchical search of restrictions on a number of key fiscal indicators clearly demonstrates the process of smoothing conflicts and resolving problems encountered during the adaptation of target strategic requirements to the real possibilities of the enterprise.

Benchmarking fold detection by DaliLite v.5

Motivation Protein structure comparison plays a fundamental role in understanding the evolutionary relationships between proteins. Here, we release a new version of the DaliLite standalone software. The novelties are hierarchical search of the structure database organized into sequence based clusters, and remote access to our knowledge base of structural neighbors. The detection of fold, superfamily and family level similarities by DaliLite and state-of-the-art competitors was benchmarked against a manually curated structural classification. Results Database search strategies were evaluated using Fmax with query-specific thresholds. DaliLite and DeepAlign outperformed TM-score based methods at all levels of the benchmark, and DaliLite outperformed DeepAlign at fold level. Hierarchical and knowledge-based searches got close to the performance of systematic pairwise comparison. The knowledge-based search was four times as efficient as the hierarchical search. The knowledge-based search dynamically adjusts the depth of the search, enabling a trade-off between speed and recall. Availability and implementation http://ekhidna2.biocenter.helsinki.fi/dali/README.v5.html. Supplementary information Supplementary data are available at Bioinformatics online.