### Table 1. Execution times required by the iterative decimation process and the one based on union and nd operations.

"... In PAGE 6: ... Note that the number jSDlj of initial darts is, in this case a linear function of the size 22n of the input image. This size is multiplied by 4 between each line of Table1 . The execution time of the Union amp; Find algorithm is also multiplied by 4 between each line.... ..."

### Table 3: Sample decision for execution vs. planning.

"... In PAGE 12: ... Subplans encourage modularity and re-use. Once written, a plan can be used as an operator in any number of Figure 5: Calling a subplan PLAN P2 { INPUT: w, x OUTPUT: z BODY { Op5 (w : y) P1 (x, y : z) } } Table 2: Monitoring operators Name Purpose dbquery Fetches relation from DB based on query dbappend Append to existing relation in DB dbexport Export relation to DB dbupdate Processes an update query (no results returned) email Emails data to specified e-mail address fax Faxes data to specified fax number phone Sends text message to specified cell phone number null Conditionally routes stream based on if another is empty Name Purpose wrapper Extracts web page data as relation xml2rel Converts XML document into a relation rel2xml Converts a relation to an XML document xquery Manipulates attributes that are XML documents select Filters relation based on specified criteria project Extracts specified attributes from relation join Combines relations based on specified criteria union Performs set union of two relations minus Performs set minus of two relations intersect Performs set intersect of two relations pack Embeds relation in single attribute tuple unpack Expands embedded relation from single attribute tuple Table1: Data manipulation operators Name Purpose apply Apply single row function to each relation tuple aggregate Apply multi-row function to relation Table3 : Extensibility operators... In PAGE 29: ... We are currently exploring several different versions of informed strategies. Table3 shows a simple one.... In PAGE 34: ... An example customer database. Table3 . Prescribed plans for Steve.... In PAGE 36: ... The case base quality is defined as the total cost to transform all negative instances into a positive counterpart in the testing set. Our first case-base mining algorithm is described in more detail in Table3 . Given an input database, we divide the database into a training database and a testing database.... In PAGE 36: ... The training database consists of the positive instances of the original database, whereas the testing data are the negative instances. Table3 . Algorithm Centroids-CBMine (database DB, int K) Steps Begin 1 casebase= emptyset; 2 DB = RemoveIrrelevantAttributes(DB); 3 Separate the DB into DB+ and DB-; 4 Clusters+ = ApplyKMeans(DB+, K); 5 for each cluster in Clusters+, do 6 C = findCentroid(cluster); 7 Insert(C, casebase); 8 end for; 9 Return casebase; End In the algorithm Centroids-CBMine in Table 3, the input database is DB.... In PAGE 36: ... Table 3. Algorithm Centroids-CBMine (database DB, int K) Steps Begin 1 casebase= emptyset; 2 DB = RemoveIrrelevantAttributes(DB); 3 Separate the DB into DB+ and DB-; 4 Clusters+ = ApplyKMeans(DB+, K); 5 for each cluster in Clusters+, do 6 C = findCentroid(cluster); 7 Insert(C, casebase); 8 end for; 9 Return casebase; End In the algorithm Centroids-CBMine in Table3 , the input database is DB. There are two classes in this database, where the positive class corresponds to population of desired cases and the negative class the unconverted cases.... In PAGE 74: ... Figure 1 shows an example minimal annotated partially ordered plan with conditional effects. Table3 shows the template DISTILL creates to represent that plan. Note that the conditions on the generated if statement do not include all terms in the initial and goal states of the plan.... In PAGE 74: ... Similarly, b(x) and the conditional effects that could generate the term c(x) or prevent its generation are also ig- nored, since it is not relevant to achieving the goals. if (in current state (f(?0:type1)) and in current state (g(?1:type2)) and in goal state (a(?0:type1)) and in goal state (d(?1:type2))) then op1 op2 Table3 : The template DISTILL would create to represent the plan shown in Figure 1. Merging Templates The merging process is formalized in the procedure Add To Template in Table 2.... ..."

### Table 2. Results of the Union-Find benchmark.

2005

"... In PAGE 13: ... As a result, the partner constraints for all their occurrences are derived to be passive, and the updates in rules f1, l2, and l3 can be done in-place. Table2 gives an overview of the results. The original version of the program is called union-find, and the version with two extra simpli cation rules is called union-find-2.... ..."

### Table 1. The proposed fast motion estimation algorithm.

"... In PAGE 2: ... In the following stages, CX BP C2 are used for computing BW CX . The proposed multiresolution motion estimation method algo- rithm is shown is Table1... In PAGE 4: ...0000 4. EXPERIMENTAL RESULTS The two-dimensional version of algorithm of Table1 was used for finding the best match for each block in each frame of video se- quences, from a search conducted in its neighborhood in the pre- vious frame. For the first 30 frames of the gray-scale, 8 bit-per-pixel, BFBIBCA2 BEBKBK , salesman video sequence, with blocks of size BDBI A2 BDBI,and search area of BFBF A2 BFBF (W=16), and D4 BPBE, the proposed method gives an speed up of more than 36 compared to a full search.... ..."

### Table 2. Fast full search algorithms. Motion estimation algorithm Savings

in A New Algorithm for Fast Full Search Block Motion Estimation Based on Number Theoretic Transforms

"... In PAGE 4: ... In the Table 1 the algorithmic operation counts (indexing is not accounted) and the time used are shown for the estimation of one motion vector. Table2 compares the NTT-based motion estimation algorithm to results reported in the literature for other fast full search algorithms. In the soft- ware implementation, the NTT algorithm do not appear to surpass other fast full search algorithms.... ..."

Cited by 1

### Table 1: The extended fast transversal filter for orthonor- mal bases.

"... In PAGE 4: ... (12), (27) and (21)]. Table1 shows the resulting gener- alized FTF algorithm. Note that when ak = 0, we have = I and therefore kM+1;N+1 = kM+1;N, in which case the recursions col- lapse to the FTF algorithm [6].... ..."

### Table 1 : Fast Algorithm

"... In PAGE 8: ...onstants c1 and c2; then, the solution of (3.4) and (3.6) needs O(n ln n) operations. The following tables give us the numerical results of the fast algorithm and the quadrature method, ( Table1 and Table 2, respectively) applied to equation (1.3) with k(x; t) de ned by (3.... ..."

### Table 1. Precision and recall for finding factors in cases

1999

"... In PAGE 11: ... The results of the experiments suggest that both are beneficial. As shown in Table1 , the decision tree algorithm achieved precision and recall of up... ..."

Cited by 20

### Table 12. Fast full search motion estimation algorithms Algorithm Savings NTT Algorithm Savings

2002

"... In PAGE 66: ... The 48-point algorithm minimizes the count of general multiplications, where nei- ther of the multiplicands is a constant, but nonetheless it does not perform well, since the congruent reduction after a multiplication in that case is so slow operation. The approximative comparison between different fast full search algorithms is given in Table12 , where higher percentage means higher computation savings, as compared to ESA, and thus faster algorithm. However, it should be noted that there are much more considerations than just the plain execution speed given in the table: many papers benchmark motion estimation methods with good-quality video sequences with little noise and often with little motion.... In PAGE 69: ... Unlike other fast full search algorithms, or even conventional search strategies such as Three Step Search, correlation-based algorithms (as NTT) have absolutely regular data flow, and they are therefore most suitable for ASIC implementation in this aspect. The rough comparison between different fast full search algorithms is given in Table12 , but in many applications the savings will be less for methods which are not based on corre- lation. Additionally, from many papers it is not clear how the algorithms were implemented.... ..."

Cited by 1

### Table 2. Error Rates of decision-tree-based classification.

"... In PAGE 11: ...2. Results Table2 compares the results for our decision-tree-based algorithm with the results for C4.5.... ..."