A brief view on your research interest

4. Enhanced continuous mining and tunnel-boring machine mining
Gu and Li suggested that deep metal mining should adopt the technologies of enhanced continuous mining and high in situ stress-induced fragmentation. However, four critical issues remain for deep mining in hard rocks: ① characterization of and methods to understand high-stress fields and geological structures in deep mining; ② knowledge of full-block fracturing for hard rock under high in situ stresses; ③ support measures to control rockburst at high temperatures; and ④ knowledge of the flow and coupling of the integrated solid-gas-liquid medium in infiltration mining for low-grade mineral deposits.
The use of tunnel-boring machines (TBMs) in mining applications is difficult, due to the complex heterogeneity of the target rock mass. Over 70% of TBM failure in mines is due to geology-related problems . In recent years, there has been an increase in the use of TBMs and in the average drill length for tunneling in hard rock mines. However, several limitations still restrict the use of TBMs in mining applications. Spalling or rockburst due to stress redistribution in highly stressed rock is a major drawback during TBM cutting in hard rock mines, and can affect safety and tunnel support installation. This issue has been alleviated in the recent Jinping II hydropower plant project by monitoring microseismic activity and tunnel deformation in order to effectively predict and avoid rockburst in tunneling. Highly fractured and blocky rock masses are another factor inhibiting the use of TBM cutters in a mining application. Loose chunks of rock are known to jam and damage front transfer chutes and cutter mounting buckets. Therefore, to extend the application of TBMs in deep mining, TBMs need modifications such as impact bars to avoid damage to cutters, mucking buckets, and belt conveyors.
In addition to the problems encountered in hard rock mines, other complications associated with water inrush and methane explosions affect the use of TBM cutters in coal mines. A novel integrated drilling-slotting technique has been implemented in the Pingdingshan coalfield in China for coal-methane co-exploitation; this technique enhances both coal and methane recovery, while reducing the possibility of methane explosions. Groundwater borne in adverse geological bodies such as faults and karst caves can cause coal mine collapse.
The modern world generates enormous amounts of data, and it is growing year by year. Data has become the most valuable managerial resource to provide a competitive edge and originate knowledge management initiatives. Now manual data processing and classification has become costly and ineffective — and it has to be either automated entirely or used only when the important data is already selected automatically from the total quantity. Text mining is essentially the automated process of deriving high-quality information from text. Its main difference from other types of data analysis is that the input data is not formalized in any way, which means it cannot be described with a simple mathematical function. Text analysis, machine learning, and big data are now available to larger number of companies, but there is still not enough information about these methods and their benefits for business. In our article we want to contribute to this topic by exploring what challenges can be addressed by text mining and what applications we at WaveAccess have developed using this technology.
Basic text mining tools
Just in a few steps text mining systems extract key knowledge from a corpus of texts, decide whether any given text is related to the designated subject, and reveal the details of its contents.
Document relevance (searching for texts relevant to the given subject). The subject can be quite narrow, such as academic papers on eye surgery.
Named entities. If a document is considered relevant, one may need to find any specific entities in it — like the academics’ names, or diseases.
Document type. A document can be tagged based on its content. For example, product reviews can be classified as positive or negative.
Entity linking. Besides the facts themselves, it is often crucial to find the exact parts in documents that link the facts together — like the relation between a drug and its side effects, or between a person name and negative reviews of their work.
Typical text mining tasks
Text mining helps not only to extract useful knowledge from large unstructured data management projects, but also to improve their ROI. For business, this means they can use the benefits of big data without costly manual processing: just set the irrelevant data aside and get answers.
Here is just a fraction of jobs perfect for text mining:
Semantic scientific literature search
Text mining can help in making your way through a vast array of scientific publications: it finds relevant articles, saves time and money.
European and American pharmaceutical companies are legally obliged to recall their product, or modify the patient information leaflets and other related documents, if any side effects are discovered in the product. Besides the company’s own research, the main way of side effect discovery is reading scientific articles by other researchers. Due to the vast amount of articles published, it is virtually impossible to process all of them manually.
To address this problem, scientific publishers (or data analysis companies affiliated by publishers) offer the service of automated article search using algorithms and approaches as designated by the client (a pharmaceutical company). As a result, the client gets a brief report of relevant articles in the required format. Having read the report, they may choose to buy specific articles.
Priced publications
The newest scientific articles and research results are only available from publishers for a fee, starting from USD 25-30 per article.
This situation puts most American pharmaceutical companies in a tough situation. They are obliged by law to track all mentions of their products in relation to side effects, in order to modify product specifications or to recall the products from the market. But buying all the articles that might mention a medication is expensive, let alone working hours to process all texts. At WaveAccess, we have developed a solution for our client in the pharmaceutical industry to automate article searches: we made a text mining platform to search articles and their metadata. Now the client only pays for articles that most likely contain the relevant text. Tasks like this require text mining due to their complexity: for example, not all sources have standardized bibliographic data — sometimes this data has to be searched separately. Sometimes even parsing the company address from metadata employs machine learning.
Market research
Text mining apps help to figure out the media space that a company lives in and how it is received by its audience.
Companies need an unbiased appraisal of their products, as well as competing products, to build their development strategy. There are a lot of product reviews, but due to an abundance of sources (academic articles, magazines, news, product review websites), automated text processing excels here, too.
Source credibility
With text mining, it is still a tough job to tell fake reviews (especially if they are well-done) from fair and unbiased ones. So what’s the plan here?
In pharmacy, “product reviews” are medication testing results that are published in trusted academic magazines. It is much harder to publish a “fake review” due to high standards for academic papers. So usually those reviews can be trusted, and their sources are credible.
But if the goal is to analyze all publicly accessible sources (all over the internet), we have to make a credibility ranking of authors and sources to reveal fake reviews. For academic papers and authors there is such thing as a citation index (CI). We use this parameter in article searches and include it in the final report, so that the reader can decide how much should they trust the given source.

Another related but different job is sentiment analysis (also known as opinion mining). Here the goal is to estimate the author’s emotional attitude to a given object. This helps classify the reviews or figure out public opinion of the company, and has a lot more applications, too.

Knowledge management
Paperwork optimization is instrumental in understanding which data and documents are available to the company and setting up quick access to them.
As the company scales, it accumulates a lot of knowledge assets. Those assets are often poorly structured and unstandardized. Departments may use their own inner document storages, or may not have them at all, which makes finding the necessary information nearly impossible. This problem is particularly apparent when companies are merged together.
For better use of the accumulated knowledge, text mining systems may be used:
To collect and standardize data from different sources automatically
To add metadata (such as document source, authors, creation date, etc.)
To index and categorize the documents
To provide a document search interface by parameters defined by users
Such text mining systems may have user roles and authorization levels as directed by safety standards. Client service department optimization
Besides internal documents, a company gets a lot of text data from the outside, — for example, input forms and orders from a website.
Incoming requests from potential clients may often be lacking information. Sales managers spend time on request processing and client negotiations, as it is often unclear what the client wants and whether they are really interested in the offer.Text mining systems can sort incoming requests and give more information about clients and their needs. Order processing time is minimized, the client service department can serve more clients and the business can make more money overall.
How text mining increases revenue?
One of our customers is a company that does maintenance and repairs on industrial objects. They have a lot of repair categories (from road surfacing to electricity and dozens more), as well as two types of repairs:
Warranty repairs, which is free for the client
Non-warranty repairs, which makes money for the company
The company gets up to 4,000 repair requests daily, and each request is processed by a human customer service manager. Managers create repair entries in a CRM system, choosing among repair categories and types in a pop-up list. Based on the amount of requests, they also plan the workload of repair teams.
The requests are written in no particular format — so before the introduction of text mining, only a human manager could process it and fill all fields in a CRM entry, taking a lot of time. Also, it was not always obvious whether it was a warranty or a non-warranty case.
WaveAccess has developed a system that helps the customer service department sort requests better, based on unformalized text only. This system suggests some categories that are most likely to fit a particular case, and helps employees find them quickly in the CRM pop-up list.
But the most valuable aspect of the innovation was that the system now can detect non-warranty cases better, resulting in more revenue for the company.