ANALYSIS

This study specifically seeks to identify the major morphological processes in the language, analyze how they operate using examples from native speakers, identify their semantic functions, and describe the structural patterns and rules governing their use. The study is anchored on the Item- and-Arrangement theoretical framework, which explains how morphemes combine systematically to form complex words. Data for the study were collected through oral interviews with native Edo speakers and from written Edo texts. The sample population comprised adult native speakers of Edo language drawn from Benin City and its environs. The findings reveal that Edo employs several productive morphological processes such as affixation (prefixation, suffixation, circumfixation, and suprafixation), reduplication, compounding, and infixation. These processes play significant roles in deriving new words, marking grammatical categories, and extending meaning. The study further shows that tonal variation functions as a suprafix, affecting word meaning and grammatical distinctions. The need for this study arises from the relative lack of detailed morphological description of Edo despite its linguistic richness. The hypothesis that Edo morphological processes are rule-governed and structurally patterned was confirmed. In conclusion, the study contributes to the understanding of Edo morphology and provides a basis for further linguistic research on Niger-Congo language.

Time series data consists of information collected over time, often at regular intervals, which can quickly accumulate in large volumes. To analyze and present this data effectively, it needs to be stored in a way that is easy to access. Database Management Systems (DBMSs) are commonly used for this purpose. There are various types of DBMSs, each with distinct advantages and disadvantages that involves different trade-offs regarding their features. In this study, we compare the performance of two different DBMSs for managing time series data. The first is PostgreSQL, a widely used relational DBMS enhanced by the Timescale DB extension designed for time series. The second is Mongo DB, a leading NoSQL system that offers built-in support for time series data. Investigation was made to ascertain which of these DBMS is better suited for time series data by analyzing their query execution times. Two databases were setup using sample time series dataspecifically, publicly available weather. A series of test queries were conducted simulate real world scenarios and measure their execution times The results are analyzed on a query-by-query basis to highlight performance between two systems, with PostgreSQL outperforming MongoDB on some queries (by over two orders of magnitude) while MongoDB was faster on others (by more than 30 times in one case). It was concluded that certain queries and their related real-world applications may favor one DBMS over the other, based on how well the query structure aligns with the system’s strength. Additional factor was discussed that may affect each DBMSs query execution efficiency and consider potential improvements.