The noisiness and low quality of IT operations management data is a major challenge in using machine learning to assist IT operations management. Our system mitigates this challenge by automatically measuring data quality, and then using the results to select data subsets that generate improved model performance. Based on a set of metrics that quantify the quality of a corpus with both structured and unstructured data, we are proposing a framework to automatically identify "well behaved" subsets in the corpus. By streaming input data to separate models for these subsets, we can achieve better performance when compared with a model trained on the full dataset. We present a motivating example that inspired our approach as well as a deployment case study of our system based on engagements with two clients which demonstrate that the proposed methodology is effective for detecting such subsets to improve model performance.

Given a few seed entities of a certain type (e.g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds. Entity set expansion in software-related domains such as StackOverflow can benefit many downstream tasks (e.g., software knowledge graph construction) and facilitate better IT operations and service management. Meanwhile, existing approaches are less concerned with two problems: (1) How to deal with multiple types of seed entities simultaneously? (2) How to leverage the power of pre-trained language models (PLMs)? Being aware of these two problems, in this paper, we study the entity set co-expansion task in StackOverflow, which extracts Library, OS, Application, and Language entities from StackOverflow question-answer threads. During the co-expansion process, we use PLMs to derive embeddings of candidate entities for calculating similarities between entities. Experimental results show that our proposed SECoExpan framework outperforms previous approaches significantly.

Due to increased competition in the IT Services business, improving quality, reducing costs and shortening schedules has become extremely important. A key strategy being adopted for achieving these goals is the use of an asset-based approach to service delivery, where standard reusable components developed by domain experts are minimally modified for each customer instead of creating custom solutions. One example of this approach is the use of contract templates, one for each type of service offered. A compliance checking system that measures how well actual contracts adhere to standard templates is critical for ensuring the success of such an approach. This paper describes the use of document similarity measures - Cosine similarity and Latent Semantic Indexing - to identify the top candidate templates on which a more detailed (and expensive) compliance analysis can be performed. Comparison of results of using the different methods are presented.

Resource description framework (RDF) is a rapidly expanding Web standard. RDF databases attempt to track the massive amounts of Web data and services available. In this paper, we study the problem of aggregate queries. We develop an algorithm to compute answers to aggregate queries over RDF databases and algorithms to maintain views involving those aggregates. Though RDF data can be stored in a standard relational DBMS (and hence we can execute standard relational aggregate queries and view maintenance methods on them), we show experimentally that our algorithms that operate directly on the RDF representation exhibit significantly superior performance.

TAX is perhaps the best known extension of the relational algebra to handle queries to XML databases. One problem with TAX (as with many existing relational DBMSs) is that the semantics of terms in a TAX DB are not taken into account when answering queries. Thus, even though TAX answers queries with 100% precision, the recall of TAX is relatively low. Our TOSS system improves the recall of TAX via the concept of a similarity enhanced ontology (SEO). Intuitively, an ontology is a set of graphs describing relationships (such as isa, partof, etc.) between terms in a DB. An SEO also evaluates how similarities between terms (e.g. "J. Ullman", "Jeff Ullman", and "Jeffrey Ullman") affect ontologies. Finally, we show how the algebra proposed in TAX can be extended to take SEOs into account. The result is a system that provides a much higher answer quality than TAX does alone (quality is defined as the square root of the product of precision and recall). We experimentally evaluate the TOSS system on the DBLP and SIGMOD bibliographic databases and show that TOSS has acceptable performance.