Microbial degradation of textile industrial effluents

Textile waste water is a highly variable mixture of many polluting substance ranging from inorganic compounds and elements to polymers and organic products. To ensure the safety of effluents, proper technologies need to be used for the complete degradation of dyes. Traditionally, treatments of textile waste water involve physical or chemical methods. But both physical and chemical methods have many short comings. Biodegradation is an eco friendly activity it can produce little or no secondary hazard. In this work, the in situ degradation of textile industrial effluent was carried out. The degradation of two different dyes, blue and green colour has been studied. The isolated organism which showed the ability to degrade dye was characterized and identified as Paenibacillus azoreducers using various biochemical techniques. The degradation of dye was confirmed via the decolourisation assay and by the measurement of COD and BOD values. A trickling bed reactor was designed and the treatment of effluent from a textile industry was effectively carried out.Key words: Biodegradation, textile wastewater, secondary hazard, Paenibacillus azoreducens, decolourisation, trickling bed reactor

Revisting SQL Query Recommender System Using Hierarchical Classification

For analytical purposes, lots of data are gathered which are gathered and explored in data warehouses. Even to handle such a large data is a tough task for expert people. For non-expert users or for users who are not familiar with the database schema, handling such a voluminous data is more difficult task. The aim of this paper is to facilitate this class of users by recommending them SQL queries that they may use. By following the users past behavior and comparing them with other users, these SQL recommendations are selected. Initially, users may not know from where they can start their exploration. Secondly, users may overlook queries which help them to retrieve important data. Using hierarchical classification, the queries are recorded and compared which is then re-ranked according to relevance. Using users querying behavior, the relevant queries are retrieved. To issue a series of SQL queries, users use a query interface which aim to analyze the data and mine it for interesting information. DOI: 10.17762/ijritcc2321-8169.150614

Query Recommender System Using Hierarchical Classification

In data warehouses, lots of data are gathered which are navigated and explored for analytical purposes. Even for expert people, to handle such a large data is a tough task. Handling such a voluminous data is more difficult task for non-expert users or for users who are not familiar with the database schema. The aim of this paper is to help this class of users by recommending them SQL queries that they might use. These SQL recommendations are selected by tracking the users past behavior and comparing them with other users. At first time, users may not know where to start their exploration. Secondly, users may overlook queries which help to retrieve important information. The queries are recorded and compared using hierarchical classification which is then re-ranked according to relevance. The relevant queries are retrieved using users querying behavior. Users use a query interface to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. DOI: 10.17762/ijritcc2321-8169.15067