Data quality is a perennial problem for many enterprise data assets. To improve data quality, businesses often employ rule based data standardization systems in which domain experts code rules for handling important and prevalent patterns. Finding these patterns is laborious and time consuming, particularly for noisy or highly specialized data sets. It is also subjective to the persons determining these patterns. In this paper we present a tool to automatically mine patterns that can help in improving the efficiency and effectiveness of these data standardization systems. The automatically extracted patterns are used by the domain and knowledge experts for rule writing. We use a greedy algorithm to extract patterns that result in a maximal coverage of data. We further group the extracted patterns such that each group represents patterns that capture similar domain knowledge. We propose a similarity measure that uses input pattern semantics to group these patterns. We demonstrate the effectiveness of our method for standardization tasks on three real world datasets.

There is often a transient need within enterprises for data cleansing which can be satisfied by offering data cleansing as a transient service. Every time a data cleansing need arises it should be possible to provision hardware, software and staff for accomplishing the task and then dismantling the set up. In this paper we present such a system that uses virtualized hardware and software for data cleansing. We share actual experiences gained from building such a system.We use a cloud infrastructure to offer virtualized data cleansing instances that can be accessed as a service. We build a system that is scalable, elastic and configurable. Each enterprise has unique needs which makes it necessary to customize both the infrastructure and the cleansing algorithms to address these needs. In this paper we will present a system that is easily configurable to suit the data cleansing needs of an enterprise.

Address Cleansing is very challenging, particularly for geographies with variability in writing addresses. Supervised learners can be easily trained for different data sources. However, training requires labeling large corpora for each data source which is time consuming and labor intensive to create. We propose a method to automatically transfer supervision from a given labeled source to a target unlabeled source using a hierarchical dirichlet process. Each dirichlet process models data from one source. The shared component distribution across these dirichlet processes captures the semantic relation between data sources. A feature projection on the component distributions from multiple sources is used to transfer supervision.

Service desks are used by customers to report IT issues in enterprise systems. Most of these service requests are resolved by level-1 persons (service desk attendants) by providing information/quick-fix solutions to customers. For each service request, level-1 personnel uses keyword search to see if the incoming incident is duplicate of any of historic incidents; otherwise, she creates an incident ticket and, with other related information, forwards it to incidentpsilas subject matter expert (SME). Incident management, responsible for managing the life cycle of all incidents, is the key to ensure availability of enterprise systems. Timely resolution of incoming incident is required to attain availability objectives. Currently, the incident management process is largely manual, error prone and time consuming. In this paper, we present a technique to correlate the incoming incident with configuration items (CIs) stored in configuration management database (CMDB). Such a correlation can be used for correctly routing the incident to service personnel, incident investigation and root cause analysis.

With the advent of XML as the new standard for information representation and exchange, indexing and querying of XML data is of major concern. In this paper, we propose a method for representing an XML document as a sequence based on a variation of Prüfer sequences. We incorporate new components in the node encodings such as level, number of a certain kind of descendants and develop methods for holistic processing of tree pattern queries. The query processing involves converting the query also into a sequence and performing subsequence matching on the document sequence. We establish certain interesting properties of the proposed method of sequencing that give rise to a new efficient pattern matching algorithm. The sequence data is stored in a two level B+-trees to support query processing. We also propose an optimization for parent-child axis to speed up the query processing. Our approach does not require any post-processing and guarantees results that are free of false positives and duplicates. Experimental results show that our system performs significantly better than previous systems in a large number of cases.