Use of expert assessment and analytics in Russia’s innovation infrastructure

This paper presents one definition of the Russian Federation’s national security, identifies the role of scientific and technological progress as the basis for this security and shows the development of its innovation. The study treats the concept of expertology as a line of scientific and analytical activities; examines the techniques for the state expert assessment of scientific and technological goal-oriented programs, as well as their integrated support.     

The Suitability of Web Analytics Key Performance Indicators in the Academic Library Environment

As the demand for library assessment grows, academic libraries are becoming more interested in Web analytics. Data are automatically gathered and provide information about a wide variety of online interactions. Libraries have long used simple counts such as visits and page views, but have more recently begun to choose strategic benchmarks, also known as key performance indicators (KPIs). Many common KPIs were created for commercial websites and are challenging to adapt for libraries. However, the underlying concepts are sufficiently valuable that libraries should explore their use. By evaluating the validity of web metrics, libraries can further the development of standards and benchmarks to support future investigations. This article discusses how commercial web metrics might be adapted for use in academic libraries. First, the limitations of web analytics are presented. Major key performance indicators used in the commercial sector are reviewed in the academic library context. Finally, the article discusses how the various indicators might support specific library website goals and decisions and uses local data to illustrate one example case. As libraries choose web analytic methods, they should deliberately evaluate their validity. Over time, this will slowly build the profession's ability to use web analytics more effectively for library assessment.     

The use of model based recursive partitioning as an analytic tool in child welfare

Child welfare agencies are tasked with investigating allegations of child maltreatment and intervening when necessary. Researchers are turning to the field of predictive analytics to optimize data analysis and data-driven decision making. To demonstrate the utility of statistical algorithms that preceded the current predictive analytics, we used Model Based (MOB) recursive partitioning, a variant of regression analysis known as decision trees, on a dataset of cases and controls with a binary outcome of serious maltreatment (defined as hospitalization or death). We ran two models, one which split a robust set of variables significantly correlated with the outcome on the partitioning of a proxy variable for environmental poverty, and one which ran the same variable set partitioned on a variable representing confirmed prior maltreatment. Both models found that what most differentiated children was spending greater than 2% of the timeframe of interest in foster care, and that for some children, lack of Medicaid eligibility almost doubled or tripled the odds of serious maltreatment. We find that decision trees such as MOB can augment risk assessment tools and other data analyses, informing data-driven program and policy decision making. We caution that decision trees, as with any other predictive tool, must be evaluated for inherent biases that may be contained in the proxy variables and the results interpreted carefully. Predictive analytics, as a class, should be used to augment, but not replace, critical thinking in child welfare decision making.     

Efficient querying of multidimensional RDF data with aggregates: Comparing NoSQL,RDF and relational data stores

This paper proposes an approach to tackle the problem of querying large volume of statistical RDF data. Our approach relies on pre-aggregation strategies to better manage the analysis of this kind of data. Specifically, we define a conceptual model to represent original RDF data with aggregates in a multidimensional structure. A set of translations rules for converting a well-known multidimensional RDF modelling vocabulary into the proposed conceptual model is then proposed. We implement the conceptual model in six different data stores: two RDF triple stores (Jena TDB and Virtuoso), one graph-oriented NoSQL database (Neo4j), one column-oriented data store (Cassandra), and two relational databases (MySQL and PostGreSQL). We compare the querying performance, with and without aggregates, in these data stores. Experimental results, on real-world datasets containing 81.92 million triplets, show that pre-aggregation allows for reducing query runtime in all data stores. Neo4j NoSQL and relational databases with aggregates outperform triple stores speeding up to 99% query runtime.     

Analysing the tweets to examine the behavioural response of Indian citizens over the approval of national education policy 2020

Indian education regulating body introduces the National Education Policy 2020 (NEP 2020) with the objective of improving the education infrastructure. In this study, we analyse the tweets (related to NEP) intending to look for the behavioural responses of the Indian People over the approval of NEP. It interestingly points out that some of the Indian states are showing majorly negative emotions for the acceptance of policies in the new education system in the initial period of the NEP. This opposition is demonstrated in the form of agitation and starting opposing hashtags on social networks. The outcomes of this study will help the education policymakers in getting an insight into the response of Indian citizens in context to the purposed policy. They can take preventive, perfective, and corrective measures to improve and provide more clarification to the people of the regions where the negative emotions are alarming.