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Milion - New Dimension

In data warehousing, we have fact and dimension tables to store the data. Dimensional tables are used to analyze the measures in the fact tables. In a data environment, data is initiated at operational databases and data will be extracted-transformed-loaded (ETL) to the data warehouse to suit the analytical environment.

Milion - New Dimension

Customer, Product are examples for Dimensional tables. These dimension attributes are modified over time and in the data warehouse, we need to maintain the history. In operational systems, we may overwrite the modified attributes as we may not need the historical aspects of data. Since our primary target in data warehousing is to analyze data with the perspective of history, we may not be able to simply overwrite the data and we need to implement special techniques to maintain the history considering analytical and volume aspects of the data warehouse. This implementation is done using Slowly Changing Dimensions in Data Warehouse.

Type 2 Slowly Changing Dimensions in Data warehouse is the most popular dimension that is used in the data warehouse. As we discussed data warehouse is used for data analysis. If you need to analyze data, you need to accommodate historical aspects of data. Let us see how we can implement SCD Type 2.

In the above customer dimension, there are two records and let us say that customer whose CustomerCode is AW00011012, has been promoted to Senior Management. However, if you simply update the record with the new value, you will not see the previous records. Therefore, a new record will be created with a new CustomerKey and a new Designation. However, other attributes will be remaining the same.

As you can see Management designation can be seen in the above result which means that it has covered the historical aspects. Type 2 SCD is one of the implementations where you cannot avoid surrogate keys in dimensional tables in the data warehouse.

Type 3 Slowly Changing Dimension in Data warehouse is a simple implementation where history will be kept in the additional column. If we relate the same scenario that we discussed under Type 2 SCD to Type 3 SCD, the customer dimension would look like below.

For example, let us assume we want to keep the customer risk type depending on his previous payment. Since this is an attribute related to the customer, it should be stored in a customer dimension. This means every month there will be a new version of the customer record. If you have 1000 customers, you are looking at 12,000 records per month. As you can imagine this Slowly Changing Dimensions in Data Warehouse is not scalable.

SCD Type 4 is introduced in order to fix this issue. In this technique, a rapidly changing column is moved out of the dimension and is moved to a new dimension table. This new dimension is linked to the fact table as shown in the below diagram.

With the above implementation of Type 4 Slowly Changing Dimensions in Data Warehouse, you are eliminating the unnecessary volume in the main dimension. However, still you have the capabilities of performing the required analysis.

Slowly Changing Dimensions in Data Warehouse are used to perform different analyses. This article provides details of how to implement Different types of Slowly Changing Dimensions such as Type 0, Type 1, Type 2, Type 3, Type 4 and Type 6. Type 2 and Type 6 are the most commonly used dimension in a data warehouse.

I am pointing to refresh load of fact table. I have 25 milion rows of facts (last thee years) and many dimensions. One of them is SCD2 type with one history-kept field. This dimension has about 60 000 business keys and in average every business key is stored in 10 variants.

So I have to load fact table. I know that there is possibility of LOOKUP transformation with parameters , which is slow. I know that I can load into dataflow my facts and all dimension rows and conditionally find correct dimID, but it means, that if I have in average 10 versions of each business key, that I will get 9*25m useless rows in dataflow. I know about third variant ,where I can transform dimension valid_from and valid_to into rows for each date between such dates and cache such result and use it in lookup. But with my facts from last three years it means, that I have to cache something about 900 (days) * 60 000 rows.

This approach starts to feel slugish around 1,5 milion visible points (which is crazy good compared to things I tried in Python), but maybe there are some optimizations that could push this limit further.

Twin and family studies suggest that genetic factors play a role in the expression of OCD.85 Recent, advances in molecular genetics have greatly increased the capacity to localize disease genes on the human genome. These methods are now being applied to complex disorders, including OCD. Although earlier studies have indicated that the vertical transmission of OCD in families is consistent with the effects of a single major autosomal gene, it is likely that there are a number of vulnerability genes involved. One of the major difficulties in the application of these approaches is the likely etiologic heterogeneity of OCD and related phenotypes. Heterogeneity reduces the power of gene-localization methods, such as linkage analysis.86-88 Etiologic heterogeneity may be reflected in phenotypic variability, making it highly desirable to dissect the syndrome, at the level of the phenotype, into valid quantitative heritable components van Grootheest et al89 recently reviewed the twin literature and concluded that in pediatric onset OCD, OC symptoms are heritable, with genetic influences in the range of 45% to 65%. In adult onset, the evidence indicates a somewhat, lower estimate, ranging from 27% to 47%. OC symptom dimensions have rarely been evaluated in the context of twin studies, with the one exception being a recent study by van Grootheest. et al90 In this study, data from a population sample of 1383 female twins from the Virginia Twin Registry was examined OC symptoms were measured by a self-report questionnaire with 20 items from the Padua Inventory. Investigators found that each of the OC symptom dimensions shared variation with a latent common factor. Variation in this common factor was explained by both genes (36%) and environmental factors (64%). In their data only the Contamination dimension appeared to be influenced by specific genes.

Like many other psychiatric disorders, family and affected sibling studies also suggest, that genetic factors play a role in the expression of OCD. Alsobrook and colleagues91 were the first to use OC symptom dimensions in a familygenetic study. They found that, the relatives of OCD probands who had high scores on the obsessions/checking and symmetry/ordering factors were at greater risk for OCD than were relatives of probands who had low scores on those factors. The finding that relatives of OCD probands who had high scores on symmetry/ordering were at greater risk for OCD than were relatives of probands who had low scores has been replicated in a second independent family study.92

Using data collected by the Tourette Syndrome Association International Consortium for Genetics Affected Sibling Pair Study, Leckman and colleagues93 selected all available affected TS pairs and their parents for which these OC symptom dimensions (factor scores) could be generated using the four-factor algorithm. Remarkably, 50% of the siblings with TS were found to have comorbid tic-related OCD and >30% of mothers and 10% of fathers also had a diagnosis of OCD. The scores for both Factor I (aggressive, sexual, and religious obsessions and checking compulsions) and Factor II (symmetry and ordering) were significantly correlated in sibling pairs concordant, for TS. In addition, the motherchild correlations, but. not father-child correlations, were significant for these two factors. Based on the results of the complex segregation analyses, significant evidence for genetic transmission was obtained for all factors.

One elegant fMRI study113 used a symptom provocation paradigm to examine, within the same patients, the neural correlates of washing, checking, and hoarding symptom dimensions of OCD. Each of these dimensions was mediated by distinct but partially overlapping neural systems. While patients and controls activated similar brain regions in response to symptom provocation, patients showed greater activations in the bilateral ventromedial prefrontal regions (washing experiment), putamen/globus pallidus, thalamus, and dorsal cortical areas (checking experiment), left prcccntral gyrus, and right orbitofrontal cortex (hoarding experiment). These results were further supported by correlation analyses within the patient group, which revealed highly specific positive associations between subjective anxiety, questionnaire scores, and neural response in each experiment. Another recent, study114 demonstrated that eight patients with predominant washing symptoms showed increased neural responses to disgusting (but not fearful) faces, compared with nonwashing OCD patients (n=8) and healthy controls (n=19). Specifically, washers showed greater activation in the left ventrolateral prefrontal cortex (Brodmann area 47) compared with the other two groups. Finally, a study by Rauch and colleagues115 tested for associations between OCD symptom factors and regional brain activation during an implicit learning task. They found that activation within the right, caudate was inversely correlated with the symmetry/arranging (Factor IT) and contamination/washing (Factor III) symptom dimensions; left orbitofrontal activation was directly correlated with the scxual/rcligious/aggressive/counting factor (Factor I) symptom severity.

Today, discover a new dimension of vessel schedule information at FleetMon now makes full voyage schedules, routes and intermediate ports available for a large number of vessels, providing you with a new set of possibilities and advantages. Vessel schedules and port arrivals functionality has been vastly improved. 041b061a72

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